Triple
T8457342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vavin |
E199952
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Place Vavin |
E199952
|
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: Place Vavin | Statement: [Vavin, namedAfter, Place Vavin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Place Vavin Context triple: [Vavin, namedAfter, Place Vavin]
-
A.
Vavin
chosen
Vavin is a Paris Métro station in the 6th arrondissement, serving the Montparnasse and Jardin du Luxembourg area.
-
B.
Vauvert
Vauvert is a commune in southern France known for its location in the Gard department near the Camargue region.
-
C.
Vallette
Vallette is a district in Turin, Italy, known for hosting the modern Allianz Stadium, home of the Juventus football club.
-
D.
Taradeau
Taradeau is a small commune in the Var department of southeastern France, known for its Provençal countryside, vineyards, and proximity to the Massif des Maures.
-
E.
Tatihou
Tatihou is a small French island off the coast of Normandy known for its historic Vauban fortifications, maritime museum, and rich coastal birdlife.
- 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_69ca8318231881908fd1bc1c4d45d286 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe48f180c8190a71cf9d7248ade60 |
completed | March 31, 2026, 3:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce1dea01c481909496ebfca4e9916e |
completed | April 2, 2026, 7:42 a.m. |
Created at: March 30, 2026, 6:10 p.m.