Triple
T27519861
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vésulienne |
E694675
|
entity |
| Predicate | departmentOfRelatedPlace |
P163495
|
FINISHED |
| Object | Haute-Saône |
—
|
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: Haute-Saône | Statement: [Vésulienne, departmentOfRelatedPlace, Haute-Saône]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: departmentOfRelatedPlace Context triple: [Vésulienne, departmentOfRelatedPlace, Haute-Saône]
-
A.
relatedPlace
Indicates a relationship where one place is connected or associated with another place in a relevant or meaningful way.
-
B.
relatedDivision
Indicates that there is an organizational or structural association between two divisions, such as being counterparts, partners, or otherwise linked within a broader entity.
-
C.
basedInDepartment
Indicates that an entity operates or has its primary affiliation within a specific department.
-
D.
personAssociatedPlace
Indicates that a person has a notable connection or association with a particular place, such as residence, origin, work, or frequent presence.
-
E.
laterDepartment
Indicates that one department occurs or is considered after another in a defined ordering or sequence.
- F. None of above. chosen
Provenance (4 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_69f638d11c988190af7fd4572b08e038 |
completed | May 2, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69f63709e4848190b5cf322e06b23fb6 |
completed | May 2, 2026, 5:40 p.m. |
| PDg | Predicate description generation | batch_69f638344b148190bf0414ef7c5f1f38 |
completed | May 2, 2026, 5:45 p.m. |
Created at: April 27, 2026, 1:20 p.m.