Triple
T10186103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris–Mulhouse route |
E236911
|
entity |
| Predicate | servesCity |
P82
|
FINISHED |
| Object | Belfort |
E221880
|
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: Belfort | Statement: [Paris–Mulhouse route, servesCity, Belfort]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Belfort Context triple: [Paris–Mulhouse route, servesCity, Belfort]
-
A.
Belfort
Belfort is the surname of Jordan Belfort, the American former stockbroker, motivational speaker, and author whose high-profile fraud case inspired the film "The Wolf of Wall Street."
-
B.
Montélimar
Montélimar is a town in southeastern France, known as the "gateway to Provence" and famous for its traditional nougat confectionery.
-
C.
Dijon
Dijon is a historic city in eastern France renowned for its rich architectural heritage, former status as the capital of the Duchy of Burgundy, and its famous mustard.
-
D.
City of Belfort
chosen
The City of Belfort is a historic commune in northeastern France, known for its strategic fortress, rich military heritage, and the monumental Lion of Belfort sculpture by Frédéric Bartholdi.
-
E.
Épinal
Épinal is a historic town in northeastern France, known for its traditional image-printing industry and picturesque setting in the Vosges region.
- 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_69ca84d7260c8190bfbec36762943f37 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cded790b488190b1ed4645554873cd |
completed | April 2, 2026, 4:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d979d64a5481909be6d6bd1d8b6433 |
completed | April 10, 2026, 10:29 p.m. |
Created at: March 30, 2026, 9:12 p.m.