Triple
T2957065
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thelma |
E79956
|
entity |
| Predicate | associatedGenderUsage |
P34349
|
FINISHED |
| Object | primarily 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: primarily female | Statement: [Thelma, associatedGenderUsage, primarily female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedGenderUsage Context triple: [Thelma, associatedGenderUsage, primarily female]
-
A.
usedByGender
Indicates that something is utilized, applied, or engaged in by entities of a specified gender.
-
B.
genderUsage
Indicates how a particular gender is applied, referenced, or treated within a given context or system.
-
C.
hasTypicalGenderAssociation
chosen
Indicates that one entity is commonly or culturally associated with a particular gender more than with other genders.
-
D.
hasGenderNeutrality
Indicates that something (such as a term, form, or expression) is neutral with respect to gender and does not specify or imply any particular gender.
-
E.
usesGenderAccurateLanguage
Indicates that the language employed in the context correctly reflects and respects the gender identities of the entities referenced.
- 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_69ad8b1276588190a374a0b12e0f7bdf |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad992998f08190ac9428310983172e |
completed | March 8, 2026, 3:43 p.m. |
| PD | Predicate disambiguation | batch_69ad960c5c8881909d679912bd7d78f3 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:57 p.m.