Triple
T8883495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diocese of Nanterre |
E211467
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Nanterre |
E252395
|
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: Nanterre | Statement: [Diocese of Nanterre, locatedIn, Nanterre]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nanterre Context triple: [Diocese of Nanterre, locatedIn, Nanterre]
-
A.
Nanterre
chosen
Nanterre is a western suburb of Paris in the Hauts-de-Seine department of France, known as an important administrative and educational center.
-
B.
Étampes
Étampes is a historic commune and former royal town in northern France, located in the Essonne department in the Île-de-France region.
-
C.
Créteil
Créteil is a southeastern suburb of Paris and the administrative center of the Val-de-Marne department in northern France.
-
D.
Bezons
Bezons is a suburban commune in the northwestern outskirts of Paris, located in the Val-d'Oise department of the Île-de-France region in northern France.
-
E.
Fontenay-en-Parisis
Fontenay-en-Parisis is a small commune in the Val-d'Oise department in the Île-de-France region of northern France.
- 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_69e343941ae481909489cf7a4abdba68 |
completed | April 18, 2026, 8:40 a.m. |
Created at: March 30, 2026, 6:53 p.m.