Triple
T6360705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | British earldoms |
E143099
|
entity |
| Predicate | femaleTitleHolderCalled |
P19475
|
FINISHED |
| Object | countess |
—
|
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: countess | Statement: [British earldoms, femaleTitleHolderCalled, countess]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: femaleTitleHolderCalled Context triple: [British earldoms, femaleTitleHolderCalled, countess]
-
A.
officeHolderTitleWhenFemale
chosen
Indicates the specific title used for a person holding an office when that office holder is female.
-
B.
motherTitle
Indicates the formal title or honorific associated with a person's mother.
-
C.
femaleAbbreviation
Indicates that one entity is an abbreviation or shortened form specifically denoting a female version of another entity.
-
D.
titleHolderMother
Indicates that the subject is the mother of the current or specified holder of a particular title.
-
E.
hasGenderedTitle
Indicates that an entity is associated with a title or form of address that is explicitly marked for a particular gender.
- 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_69c008d7a9c4819098d647ec47776917 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067fa0d0c819098d01545849142fc |
completed | March 22, 2026, 10:06 p.m. |
| PD | Predicate disambiguation | batch_69c060ec091c8190912aac44e1b8b1c9 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:32 p.m.