Triple
T6716010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dijon campus |
E153268
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Dijon |
E72631
|
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: Dijon | Statement: [Dijon campus, locatedIn, Dijon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dijon Context triple: [Dijon campus, locatedIn, Dijon]
-
A.
Dijon
chosen
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.
-
B.
Mâcon
Mâcon is a historic town in eastern France’s Burgundy region, known for its wine production and picturesque setting along the Saône River.
-
C.
Bourg-en-Bresse
Bourg-en-Bresse is a historic town in eastern France known as the capital of the Ain department, noted for its Renaissance architecture and the royal monastery of Brou.
-
D.
Troyes
Troyes is a historic city in northeastern France, known for its well-preserved medieval old town, half-timbered houses, and Gothic churches.
-
E.
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."
- 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_69c68809b4608190a2509ddb5ab87f05 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d125db3c8190aad28919226a16da |
completed | March 27, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8d66fdb1c81909ca125e5918b0997 |
completed | March 29, 2026, 7:36 a.m. |
Created at: March 27, 2026, 2:07 p.m.