Triple
T12385525
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erlangen-Höchstadt |
E295851
|
entity |
| Predicate | vehicleRegistrationCode |
P1173
|
FINISHED |
| Object |
ERH
ERH is the vehicle registration code used on license plates for the Erlangen-Höchstadt district in Bavaria, Germany.
|
E979396
|
NE FINISHED |
How this triple was built (4 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: ERH | Statement: [Erlangen-Höchstadt, vehicleRegistrationCode, ERH]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ERH Context triple: [Erlangen-Höchstadt, vehicleRegistrationCode, ERH]
-
A.
EHRD
EHRD is the ICAO airport code for Rotterdam The Hague Airport in the Netherlands.
-
B.
ERI
ERI is the Earthquake Research Institute of the University of Tokyo, a leading Japanese center for seismology and earthquake-related research.
-
C.
ERI
ERI is the former stock ticker symbol for Eldorado Resorts, a U.S.-based casino and hospitality company that later became part of Caesars Entertainment.
-
D.
ERNE
ERNE is a scientific instrument aboard the SOHO spacecraft designed to study energetic particles emitted by the Sun.
-
E.
ERF
ERF is the stock ticker symbol used to represent Eurofins Scientific on financial markets.
- 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: ERH Triple: [Erlangen-Höchstadt, vehicleRegistrationCode, ERH]
Generated description
ERH is the vehicle registration code used on license plates for the Erlangen-Höchstadt district in Bavaria, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ERH Target entity description: ERH is the vehicle registration code used on license plates for the Erlangen-Höchstadt district in Bavaria, Germany.
-
A.
EHRD
EHRD is the ICAO airport code for Rotterdam The Hague Airport in the Netherlands.
-
B.
ERI
ERI is the Earthquake Research Institute of the University of Tokyo, a leading Japanese center for seismology and earthquake-related research.
-
C.
ERI
ERI is the former stock ticker symbol for Eldorado Resorts, a U.S.-based casino and hospitality company that later became part of Caesars Entertainment.
-
D.
ERNE
ERNE is a scientific instrument aboard the SOHO spacecraft designed to study energetic particles emitted by the Sun.
-
E.
ERF
ERF is the stock ticker symbol used to represent Eurofins Scientific on financial markets.
- F. None of above. chosen
Provenance (5 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_69d6ad9e653c8190b1473c860ee53dae |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d93fbd489c819098233a111442762e |
completed | April 10, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62ac939bc819081629b9eef20c4e7 |
completed | May 2, 2026, 4:48 p.m. |
| NEDg | Description generation | batch_69f62c7b28588190839c35c19856d16f |
completed | May 2, 2026, 4:55 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f62e403a308190a2bba3fefc420932 |
completed | May 2, 2026, 5:02 p.m. |
Created at: April 8, 2026, 9:54 p.m.