Triple
T3015875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Émile Gallé |
E82333
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Nancy, France |
E161876
|
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: Nancy, France | Statement: [Émile Gallé, workLocation, Nancy, France]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nancy, France Context triple: [Émile Gallé, workLocation, Nancy, France]
-
A.
Nancy, France
chosen
Nancy is a historic city in northeastern France known for its elegant 18th-century architecture, especially the UNESCO-listed Place Stanislas.
-
B.
Nice, France
Nice, France is a major Mediterranean coastal city on the French Riviera known for its picturesque Promenade des Anglais, vibrant arts scene, and historic old town.
-
C.
Douai, France
Douai, France is a historic town in northern France known for its medieval belfry, legal and university traditions, and role as a regional administrative center.
-
D.
Villeblevin, France
Villeblevin, France is a small commune in north-central France best known as the place where Nobel Prize–winning writer Albert Camus died in a car accident.
-
E.
Fontainebleau, France
Fontainebleau, France is a historic town southeast of Paris best known for its vast forest and royal château, long associated with French monarchs and outdoor recreation.
- 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_69ad8b1eb53481908c39bbcd1ec104b2 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a6b37288190a6965d183ca4b08b |
completed | March 8, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1eee74390819087e48b67ed4a9f27 |
completed | March 11, 2026, 10:38 p.m. |
Created at: March 8, 2026, 3 p.m.