Triple
T26983288
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deputy Secretary-General of the United Nations |
E679661
|
entity |
| Predicate | genderOfMostOfficeHolders |
P162016
|
FINISHED |
| Object | female |
—
|
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: female | Statement: [Deputy Secretary-General of the United Nations, genderOfMostOfficeHolders, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderOfMostOfficeHolders Context triple: [Deputy Secretary-General of the United Nations, genderOfMostOfficeHolders, female]
-
A.
genderOfResidents
Indicates the gender identity or classification associated with the residents of a particular place or group.
-
B.
incumbentGender
Indicates the gender of the person currently holding a particular position or office.
-
C.
officeHoldersGenderEligibility
Indicates the gender-based criteria that determine who is eligible to hold a particular office or position.
-
D.
genderOfMembers
Indicates the gender or genders associated with the members of a group or organization.
-
E.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
- 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_69eeeb5138ac8190b3c273ddc659a54f |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f622abdfac8190988421c946411d7e |
completed | May 2, 2026, 4:13 p.m. |
| PD | Predicate disambiguation | batch_69f620e0b37481909a280574decbd443 |
completed | May 2, 2026, 4:05 p.m. |
| PDg | Predicate description generation | batch_69f621c7d3e0819095b1f327637ae4f9 |
completed | May 2, 2026, 4:09 p.m. |
Created at: April 27, 2026, 6:47 a.m.