Triple
T18654629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | County of Dreux |
E456026
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Perche |
—
|
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: Perche | Statement: [County of Dreux, borderedBy, Perche]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Perche Context triple: [County of Dreux, borderedBy, Perche]
-
A.
Perche
chosen
Perche is a historic rural region in northwestern France known for its rolling countryside, forests, and traditional manors.
-
B.
Pero
Pero is a West Chadic language spoken in parts of Nigeria.
-
C.
Pero
Pero is a municipality in the Metropolitan City of Milan in northern Italy, known for its proximity to major trade fair and exhibition centers.
-
D.
Pero
Pero is a common South Slavic diminutive form of the male given name Petar (Peter).
-
E.
Perho
Perho is a small rural municipality in western Finland known for its forests, lakes, and traditional countryside landscape.
- 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_69d8d38ea1e88190997e9b231190ba6f |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e55084012881909b9dd60565011c86 |
completed | April 19, 2026, 10 p.m. |
Created at: April 10, 2026, 11:47 a.m.