Triple
T29100156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | π/4 DQPSK |
E736616
|
entity |
| Predicate | carrierPhaseAmbiguityTolerance |
P168753
|
FINISHED |
| Object | tolerant due to differential encoding |
—
|
LITERAL FINISHED |
How this triple was built (2 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: tolerant due to differential encoding | Statement: [π/4 DQPSK, carrierPhaseAmbiguityTolerance, tolerant due to differential encoding]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: carrierPhaseAmbiguityTolerance Context triple: [π/4 DQPSK, carrierPhaseAmbiguityTolerance, tolerant due to differential encoding]
-
A.
targetBitErrorRate
Indicates the specified or required bit error rate that a communication system aims to achieve or not exceed as a performance target.
-
B.
beamEstimate
Indicates that an estimated value or state has been inferred or projected for a particular entity or situation.
-
C.
pruningTolerance
Indicates how well an entity can withstand or recover from being cut back, trimmed, or pruned.
-
D.
azimuthAccuracy
Indicates the degree of precision or allowable error in the measured or specified azimuth angle between entities.
-
E.
numberOfAntennas
Indicates the quantity of antennas that an entity possesses or is associated with.
- F. None of above. chosen
Provenance (4 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_69f077ec765c81909474c88bcc8bab43 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69f67805551c81909e016ae9e3031076 |
completed | May 2, 2026, 10:17 p.m. |
| PD | Predicate disambiguation | batch_69f675ff62c48190a634bbb8896973b9 |
completed | May 2, 2026, 10:09 p.m. |
| PDg | Predicate description generation | batch_69f676f73c3481909f01fa69851b7298 |
completed | May 2, 2026, 10:13 p.m. |
Created at: April 28, 2026, 11:11 a.m.