Triple
T28658485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Racing Métro 92 |
E725397
|
entity |
| Predicate | departmentReferenced |
P165536
|
FINISHED |
| Object | Hauts-de-Seine |
—
|
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: Hauts-de-Seine | Statement: [Racing Métro 92, departmentReferenced, Hauts-de-Seine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: departmentReferenced Context triple: [Racing Métro 92, departmentReferenced, Hauts-de-Seine]
-
A.
refersToDepartmentInRegion
Indicates that an entity is associated with, or makes reference to, a specific department located within a particular region.
-
B.
department
Indicates that one entity functions as an organizational unit or division within another, typically larger, entity.
-
C.
laterDepartment
Indicates that one department occurs or is considered after another in a defined ordering or sequence.
-
D.
departmentNumber
Indicates the specific numeric code assigned to identify a particular department within an organization or system.
-
E.
departmentType
Indicates the classification or category of a department, specifying what kind of department it is.
- 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_69f01d84f5f0819087ab5e6143b14ed7 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69f6596b210481908af6cd555748f75b |
completed | May 2, 2026, 8:07 p.m. |
| PD | Predicate disambiguation | batch_69f65760fd3081908ffe014a5e2bf069 |
completed | May 2, 2026, 7:58 p.m. |
| PDg | Predicate description generation | batch_69f658ebeca4819096beb3f98f73fe31 |
completed | May 2, 2026, 8:05 p.m. |
Created at: April 28, 2026, 4:56 a.m.