Triple
T25588748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dilek İmamoğlu |
E641460
|
entity |
| Predicate | marriedToOfficeHolderOf |
P116912
|
FINISHED |
| Object | Mayor of Istanbul |
—
|
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: Mayor of Istanbul | Statement: [Dilek İmamoğlu, marriedToOfficeHolderOf, Mayor of Istanbul]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedToOfficeHolderOf Context triple: [Dilek İmamoğlu, marriedToOfficeHolderOf, Mayor of Istanbul]
-
A.
marriedToPolitician
chosen
Indicates that a person is married to someone who holds or has held a political office or role.
-
B.
marriedToHeadOfGovernmentOf
Indicates that one entity is the spouse of the person who holds the position of head of government of the other entity.
-
C.
marriedToDuringOffice
Indicates that one person was married to another person specifically during the time they held a particular office or position.
-
D.
marriedToVicePresident
Indicates that one person is legally married to an individual who holds the office or role of vice president.
-
E.
spouseOfFormer
Indicates that one entity is the spouse of another entity who is a former holder of some role, status, or position.
- 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_69e75dc42b588190a98b58e0df359674 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f627aedf548190bc9f53c8a2d67b50 |
completed | May 2, 2026, 4:34 p.m. |
| PD | Predicate disambiguation | batch_69f623a4e1048190bbb8dd1253fdcee9 |
completed | May 2, 2026, 4:17 p.m. |
Created at: April 21, 2026, 4:18 p.m.