Triple
T6779598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mayor of Amsterdam |
E155645
|
entity |
| Predicate | officeHolderGender |
P37251
|
FINISHED |
| Object | first female office holder: Femke Halsema |
—
|
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: first female office holder: Femke Halsema | Statement: [Mayor of Amsterdam, officeHolderGender, first female office holder: Femke Halsema]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officeHolderGender Context triple: [Mayor of Amsterdam, officeHolderGender, first female office holder: Femke Halsema]
-
A.
incumbentGender
Indicates the gender of the person currently holding a particular position or office.
-
B.
genderOfFirstHolder
chosen
Indicates that the relationship specifies the gender of the first entity that holds or possesses something in the described context.
-
C.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
D.
officeHolderIs
Indicates that one entity serves as the office holder (e.g., official or position occupant) of another entity, such as an office, role, or institution.
-
E.
officeHolderOf
Indicates that a person holds or has held an official position or role within a specified organization, institution, or office.
- 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_69c688162bf8819088b664b5c3b5be7a |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d26a1634819099b8a3b3196a306a |
completed | March 27, 2026, 6:54 p.m. |
| PD | Predicate disambiguation | batch_69c6d095dcac8190bb9b943f50a7f885 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:14 p.m.