Triple
T20134980
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arpitanie |
E491001
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Forez |
—
|
NE NERFINISHED |
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: Forez | Statement: [Arpitanie, hasPart, Forez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Forez Context triple: [Arpitanie, hasPart, Forez]
-
A.
Forez
chosen
Forez is a historic region in central France, corresponding largely to the plains and hills around Montbrison in today’s Loire department.
-
B.
Forst
Forst is a small settlement in the municipality of Grabs in the Swiss canton of St. Gallen.
-
C.
de Forest
de Forest is a surname most notably associated with Lee de Forest, an American inventor and early pioneer of radio and electronic communication.
-
D.
Kowen Forest
Kowen Forest is a large pine plantation and recreational forest area east of Canberra in the Australian Capital Territory, popular for activities such as mountain biking, trail running, and orienteering.
-
E.
Froyle
Froyle is a small rural village in Hampshire, England, known for its historic church and traditional English countryside setting.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da62651a0c8190a3e05e95e056a66b |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e66766e46c81908721fd47066dc9f8 |
completed | April 20, 2026, 5:50 p.m. |
Created at: April 11, 2026, 11:32 p.m.