Triple
T5494872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tournoi de France |
E144186
|
entity |
| Predicate | hostCity |
P1798
|
FINISHED |
| Object | Lyon |
E15889
|
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: Lyon | Statement: [Tournoi de France, hostCity, Lyon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lyon Context triple: [Tournoi de France, hostCity, Lyon]
-
A.
Lyon
chosen
Lyon is a major city in east-central France known for its historical and architectural landmarks, gastronomy, and role as a key economic and cultural center.
-
B.
Lyons
Lyons is a small city in southeastern Georgia, United States, known as the administrative and commercial hub of Toombs County.
-
C.
Lyons
Lyons is a sports team or athletic program associated with Wheaton College, commonly referred to by this shortened name.
-
D.
Clermont-Ferrand
Clermont-Ferrand is a central French city known for its historic cathedral built of black volcanic stone and as the longtime headquarters of the tire company Michelin.
-
E.
Rodez
Rodez is a historic cathedral city in southern France that serves as the capital of the Aveyron department in the Occitanie 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_69c008f5a2748190bce7a39aabf87a6d |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01b8c05ac8190999f84c33719d794 |
completed | March 22, 2026, 4:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11c83f8bc8190bdf139c9da2dd66b |
completed | March 23, 2026, 10:57 a.m. |
Created at: March 22, 2026, 3:31 p.m.