Triple
T5189043
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mortefontaine, France |
E117103
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Senlis |
E419845
|
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: Senlis | Statement: [Mortefontaine, France, nearbyCity, Senlis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Senlis Context triple: [Mortefontaine, France, nearbyCity, Senlis]
-
A.
Senlis
chosen
Senlis is a historic town in northern France known for its medieval architecture and its role in events such as the 14th-century Jacquerie peasant revolt.
-
B.
Châteaudun
Châteaudun is a historic town in north-central France known for its medieval château overlooking the Loir River and its role as a gateway to the Loire Valley.
-
C.
Angeville
Angeville is a small commune in the Tarn-et-Garonne department in southern France.
-
D.
Dreux
Dreux is a historic town in northern France known for its royal chapel and role as a regional center in the Eure-et-Loir department.
-
E.
Blois
Blois is a historic city in central France known for its Renaissance château, picturesque setting on the Loire River, and rich royal 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_69bd44620ff48190bcac01782107a397 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd79c732b48190af62dfffcbc5e3a6 |
completed | March 20, 2026, 4:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bee08af954819080dbe7ea1ac6ddb0 |
completed | March 21, 2026, 6:16 p.m. |
Created at: March 20, 2026, 1:46 p.m.