Triple
T9682187
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bernay |
E234308
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Évreux |
E585463
|
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: Évreux | Statement: [Bernay, locatedNear, Évreux]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Évreux Context triple: [Bernay, locatedNear, Évreux]
-
A.
Evreux
chosen
Evreux is a historic town in northern France, known for its Gothic cathedral and role as the capital of the Eure department in Normandy.
-
B.
Bourges
Bourges is a historic city in central France known for its well-preserved medieval architecture and its UNESCO-listed Gothic cathedral, Saint-Étienne.
-
C.
Blois
Blois is a historic city in central France known for its Renaissance château, picturesque setting on the Loire River, and rich royal heritage.
-
D.
Melun
Melun is a historic commune in the Île-de-France region of north-central France, known as a regional administrative center and former royal town southeast of Paris.
-
E.
Alençon
Alençon is a historic town in northwestern France renowned for its fine lace-making tradition and architectural heritage.
- 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_69ca84c99e34819092e5563a7106cfca |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9c9fec1c8190b2626848cb2c1871 |
completed | April 1, 2026, 10:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2cb2aaef481908a7be61bbfc3b008 |
completed | April 5, 2026, 8:50 p.m. |
Created at: March 30, 2026, 8:16 p.m.