Triple
T9219039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris–Cherbourg railway |
E221312
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Bayeux |
E105080
|
NE 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: Bayeux | Statement: [Paris–Cherbourg railway, passesThrough, Bayeux]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bayeux Context triple: [Paris–Cherbourg railway, passesThrough, Bayeux]
-
A.
Bayeux
Bayeux is a municipality in the Brazilian state of Paraíba, located in the northeastern region of the country and forming part of the João Pessoa metropolitan area.
-
B.
Bayeux
chosen
Bayeux is a historic town in Normandy, France, renowned for the medieval Bayeux Tapestry and its proximity to the D-Day landing beaches.
-
C.
Beauvais
Beauvais is a historic city in northern France known for its impressive Gothic cathedral and role as the capital of the Oise department.
-
D.
Calais
Calais is a major French port city on the northern coast, serving as one of the primary crossing points between France and England.
-
E.
Calais
Calais is a figure from Greek mythology, one of the winged sons of Boreas who joined Jason and the Argonauts on their legendary voyage.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca83eae42c8190a0ea9e040710a277 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccda730f688190b64b2cc8c4898ac3 |
completed | April 1, 2026, 8:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0662c28648190979cf786fc35ab75 |
completed | April 4, 2026, 1:15 a.m. |
Created at: March 30, 2026, 7:27 p.m.