Triple
T2305278
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Queen of the Movies |
E51823
|
entity |
| Predicate | hasGenderConnotation |
P34349
|
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: [Queen of the Movies, hasGenderConnotation, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderConnotation Context triple: [Queen of the Movies, hasGenderConnotation, female]
-
A.
hasTypicalGenderAssociation
chosen
Indicates that one entity is commonly or culturally associated with a particular gender more than with other genders.
-
B.
hasGenderDistinction
Indicates that a relationship, classification, or linguistic form differentiates entities based on gender categories.
-
C.
hasGrammaticalGender
Indicates that one entity assigns or possesses a specific grammatical gender in relation to another entity (such as a word, phrase, or linguistic unit).
-
D.
hasGenderFocus
Indicates that something is specifically concerned with, oriented toward, or primarily addressing a particular gender or gender-related issues.
-
E.
hasNeutralPronoun
Indicates that an entity is referred to using a gender-neutral pronoun.
- 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_69a88b0bb30c81908ded03b006d29387 |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abce1f4f0c8190a714e4dcb8449f7e |
completed | March 7, 2026, 7:05 a.m. |
| PD | Predicate disambiguation | batch_69abc58ce2a081908ce2f0cadd92e9f8 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:49 p.m.