Triple
T10217201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Picardy |
E242475
|
entity |
| Predicate | hasHistoricalProvince |
P5057
|
FINISHED |
| Object | Thiérache |
E258527
|
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: Thiérache | Statement: [Picardy, hasHistoricalProvince, Thiérache]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thiérache Context triple: [Picardy, hasHistoricalProvince, Thiérache]
-
A.
Thiérache
chosen
Thiérache is a rural, historically fortified region in northern France known for its bocage landscapes, brick churches, and traditional dairy production.
-
B.
Cottévrard
Cottévrard is a small commune in the Seine-Maritime department of the Normandy region in northern France.
-
C.
Riorges
Riorges is a commune in central France, near Roanne in the Loire department, known for its residential character and local cultural life.
-
D.
Vosgien
Vosgien is a regional dialect of the Lorrain language spoken in the Vosges area of northeastern France.
-
E.
Creuse
Creuse is a rural department in central France known for its sparsely populated landscapes, traditional agriculture, and part of the historic Limousin region.
- 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_69d381ae26c48190985abd0e25ee5d04 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d3aa6e544c8190961cdd7f1fbe24e6 |
completed | April 6, 2026, 12:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d79490a3c48190a58bff2f63e5873d |
completed | April 9, 2026, 11:59 a.m. |
Created at: April 6, 2026, 11:06 a.m.