Triple
T8883722
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 4 (Paris Métro) |
E211472
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | 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: Vavin | Statement: [Line 4 (Paris Métro), hasStation, Vavin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vavin Context triple: [Line 4 (Paris Métro), hasStation, Vavin]
-
A.
Vavin
chosen
Vavin is a Paris Métro station in the 6th arrondissement, serving the Montparnasse and Jardin du Luxembourg area.
-
B.
Vaudesir
Vaudésir is one of the prestigious Grand Cru vineyard sites in the Chablis wine region of Burgundy, renowned for producing some of its most refined and age-worthy Chardonnay wines.
-
C.
Liré
Liré is a village in western France, historically notable as the birthplace of Renaissance poet Joachim du Bellay.
-
D.
Vauvert
Vauvert is a commune in southern France known for its location in the Gard department near the Camargue region.
-
E.
Velay
Velay is a historical and geographical region in south-central France, known for its volcanic landscapes, traditional agriculture, and medieval heritage within the broader Massif Central area.
- 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_69ca838f9e20819096ab1f236a70381a |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc616b2d988190b923ef1e33aab787 |
completed | April 1, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfabd254148190b5ea3d308fe96851 |
completed | April 3, 2026, noon |
Created at: March 30, 2026, 6:53 p.m.