Triple
T6938368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | General Mayor of Bucharest |
E160607
|
entity |
| Predicate | hasSubordinateOffices |
P25079
|
FINISHED |
| Object | deputy mayors of Bucharest |
—
|
LITERAL FINISHED |
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: deputy mayors of Bucharest | Statement: [General Mayor of Bucharest, hasSubordinateOffices, deputy mayors of Bucharest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubordinateOffices Context triple: [General Mayor of Bucharest, hasSubordinateOffices, deputy mayors of Bucharest]
-
A.
hasBranchOffice
Indicates that one organization maintains a branch office or subsidiary location in another place or entity.
-
B.
hasSupportingOffice
Indicates that an entity is associated with or served by a particular office that provides support or administrative services to it.
-
C.
hasAssociatedOffice
Indicates that an entity is linked to or connected with a particular office in an official or functional capacity.
-
D.
hasSubsidiaryLabel
chosen
Indicates that one entity is identified as a subsidiary or subordinate organization of another entity.
-
E.
hasOfficeCluster
Indicates that an entity is associated with or belongs to a specific group or cluster of office locations.
- 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_69c6884f3db4819080ad65da69386206 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e0c74fe48190aeaa018631e52ef6 |
completed | March 27, 2026, 7:55 p.m. |
| PD | Predicate disambiguation | batch_69c6d7bd5a388190a57a96d925696ff6 |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:28 p.m.