Triple
T4929137
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Simone Weil |
E110648
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Marseille, France |
E15143
|
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: Marseille, France | Statement: [Simone Weil, residence, Marseille, France]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marseille, France Context triple: [Simone Weil, residence, Marseille, France]
-
A.
Marseille
chosen
Marseille is a historic Mediterranean port city in southern France known for its diverse culture, maritime heritage, and role as a major economic hub.
-
B.
Montpellier, France
Montpellier, France is a historic and vibrant city in southern France near the Mediterranean coast, known for its medieval architecture, large student population, and role as a regional cultural and economic center.
-
C.
Toulon
Toulon is a major port city on France’s Mediterranean coast that serves as the principal base of the French Navy.
-
D.
City of Nice
The City of Nice is a major coastal city on the French Riviera, renowned for its Mediterranean climate, historic old town, and rich artistic and cultural heritage.
-
E.
Marignane, France
Marignane, France is a commune in the Bouches-du-Rhône department in southern France, known as a major hub of the aerospace and helicopter industry.
- 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_69bd4415190c8190817bee7ec9f9f944 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd7038c12c81908a793b4a8768c28a |
completed | March 20, 2026, 4:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be923eac848190b8511b8027c87ff3 |
completed | March 21, 2026, 12:42 p.m. |
Created at: March 20, 2026, 1:30 p.m.