Triple
T27519860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vésulienne |
E694675
|
entity |
| Predicate | regionOfRelatedPlace |
P86016
|
FINISHED |
| Object | Bourgogne-Franche-Comté |
—
|
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: Bourgogne-Franche-Comté | Statement: [Vésulienne, regionOfRelatedPlace, Bourgogne-Franche-Comté]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionOfRelatedPlace Context triple: [Vésulienne, regionOfRelatedPlace, Bourgogne-Franche-Comté]
-
A.
relatedPlace
Indicates a relationship where one place is connected or associated with another place in a relevant or meaningful way.
-
B.
regionFrom
Indicates that something originates from, is derived from, or is associated with a particular geographic or administrative region.
-
C.
regionOfAssociation
chosen
Indicates a broader geographic or spatial area with which an entity is functionally, contextually, or organizationally associated.
-
D.
departmentOfRelatedPlace
Indicates that one place is an administrative or organizational department within, or associated with, another related place.
-
E.
regionallyAssociatedWith
Indicates that two entities are connected or related based on sharing the same or overlapping geographic or regional context.
- F. None of above.
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_69ef538550208190aa9de8e2cb260d93 |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69f7cec454a88190a9f3bbee2b856636 |
completed | May 3, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69f7c8977c288190997a892ec5f756ed |
completed | May 3, 2026, 10:13 p.m. |
Created at: April 27, 2026, 1:20 p.m.