Triple
T11043313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monument station |
E261072
|
entity |
| Predicate | hasStationCode |
P1289
|
FINISHED |
| Object |
ZMG
ZMG is the National Rail station code assigned to Monument station in Newcastle upon Tyne, England.
|
E901001
|
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: ZMG | Statement: [Monument station, hasStationCode, ZMG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ZMG Context triple: [Monument station, hasStationCode, ZMG]
-
A.
MZG
MZG is the IATA airport code for Penghu Airport, which serves the Penghu (Pescadores) archipelago in Taiwan.
-
B.
MZG
MZG is the vehicle registration code used on license plates for vehicles registered in the town of Wadern in Germany.
-
C.
ZG
ZG is the vehicle registration code used on license plates for the city of Zagreb, the capital of Croatia.
-
D.
ZMH
ZMH is the three-letter station code used to identify Mansion House Underground station on the London Underground network.
-
E.
ZMH
ZMH is the former stock ticker symbol for Zimmer Holdings, a major medical device company specializing in orthopedic products such as joint replacement implants.
- 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: ZMG Triple: [Monument station, hasStationCode, ZMG]
Generated description
ZMG is the National Rail station code assigned to Monument station in Newcastle upon Tyne, England.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ZMG Target entity description: ZMG is the National Rail station code assigned to Monument station in Newcastle upon Tyne, England.
-
A.
MZG
MZG is the IATA airport code for Penghu Airport, which serves the Penghu (Pescadores) archipelago in Taiwan.
-
B.
MZG
MZG is the vehicle registration code used on license plates for vehicles registered in the town of Wadern in Germany.
-
C.
ZG
ZG is the vehicle registration code used on license plates for the city of Zagreb, the capital of Croatia.
-
D.
ZMH
ZMH is the three-letter station code used to identify Mansion House Underground station on the London Underground network.
-
E.
ZMH
ZMH is the former stock ticker symbol for Zimmer Holdings, a major medical device company specializing in orthopedic products such as joint replacement implants.
- 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_69d6aa979bdc8190bf0e79104cc098c1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7982d42bc81908ac10f54a7b43fb7 |
completed | April 9, 2026, 12:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3a9f180688190ab2d1142b30a2836 |
completed | April 18, 2026, 3:57 p.m. |
| NEDg | Description generation | batch_69e3ad024ee88190948d5d1c327fd063 |
completed | April 18, 2026, 4:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e3b1fff754819092d634f46fb42387 |
completed | April 18, 2026, 4:32 p.m. |
Created at: April 8, 2026, 9:26 p.m.