Triple
T3453053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DVB-T2 |
E72835
|
entity |
| Predicate | usesErrorCorrection |
P22596
|
FINISHED |
| Object |
LDPC
LDPC (Low-Density Parity-Check) is a powerful class of linear error-correcting codes known for near-Shannon-limit performance and widespread use in modern high-throughput communication systems.
|
E358499
|
NE FINISHED |
How this triple was built (5 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: LDPC | Statement: [DVB-T2, usesErrorCorrection, LDPC]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LDPC Context triple: [DVB-T2, usesErrorCorrection, LDPC]
-
A.
Algebraic Coding Theory
Algebraic Coding Theory is a foundational mathematical text that systematically develops the theory and applications of error-correcting codes using algebraic methods.
-
B.
Viterbi algorithm
The Viterbi algorithm is a dynamic programming method used to find the most likely sequence of hidden states in probabilistic models such as Hidden Markov Models, widely applied in fields like digital communications, speech recognition, and bioinformatics.
-
C.
Scott encoding
Scott encoding is a method in lambda calculus for representing algebraic data types and their pattern matching behavior using higher-order functions.
-
D.
OFDM
OFDM (Orthogonal Frequency-Division Multiplexing) is a digital multi-carrier modulation technique that splits data across many closely spaced orthogonal subcarriers to improve robustness against interference and multipath fading in wireless and wired communication systems.
-
E.
Golomb
Golomb is a station on the Carmelit underground funicular system in Haifa, Israel.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: LDPC Triple: [DVB-T2, usesErrorCorrection, LDPC]
Generated description
LDPC (Low-Density Parity-Check) is a powerful class of linear error-correcting codes known for near-Shannon-limit performance and widespread use in modern high-throughput communication systems.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: LDPC Target entity description: LDPC (Low-Density Parity-Check) is a powerful class of linear error-correcting codes known for near-Shannon-limit performance and widespread use in modern high-throughput communication systems.
-
A.
Algebraic Coding Theory
Algebraic Coding Theory is a foundational mathematical text that systematically develops the theory and applications of error-correcting codes using algebraic methods.
-
B.
Viterbi algorithm
The Viterbi algorithm is a dynamic programming method used to find the most likely sequence of hidden states in probabilistic models such as Hidden Markov Models, widely applied in fields like digital communications, speech recognition, and bioinformatics.
-
C.
Scott encoding
Scott encoding is a method in lambda calculus for representing algebraic data types and their pattern matching behavior using higher-order functions.
-
D.
OFDM
OFDM (Orthogonal Frequency-Division Multiplexing) is a digital multi-carrier modulation technique that splits data across many closely spaced orthogonal subcarriers to improve robustness against interference and multipath fading in wireless and wired communication systems.
-
E.
Golomb
Golomb is a station on the Carmelit underground funicular system in Haifa, Israel.
- F. None of above. chosen
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesErrorCorrection Context triple: [DVB-T2, usesErrorCorrection, LDPC]
-
A.
usesForwardErrorCorrection
chosen
Indicates that one entity applies forward error correction techniques to detect and correct errors in data transmitted to or received from another entity.
-
B.
requiresCorrection
Indicates that something is identified as needing modification, adjustment, or fixing to correct an error or deficiency.
-
C.
supportsHardwareCalibration
Indicates that one entity provides the capability or functionality to perform calibration operations on another entity’s hardware.
-
D.
usesScrambling
Indicates that one entity applies a scrambling process or technique to another entity or to information associated with it.
-
E.
hasCheckDigit
Indicates that an identifier or code includes a calculated check digit used to verify its correctness or integrity.
- F. None of above.
Provenance (6 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ad85b12a908190a1d10a6b03b4f8ae |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbaa2f5ec81909ced93c01e8fe38b |
completed | March 8, 2026, 6:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b360ef69308190a11f37ddbf3bbc7b |
completed | March 13, 2026, 12:57 a.m. |
| NEDg | Description generation | batch_69b361958fd88190bd4a8d9837af6610 |
completed | March 13, 2026, 1 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b362310884819082a59ab92fe05fdd |
completed | March 13, 2026, 1:02 a.m. |
| PD | Predicate disambiguation | batch_69adae041d588190a84a02bca94adec8 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:16 p.m.