Triple
T26514702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | At the Noon Hour |
E669781
|
entity |
| Predicate | subjectGenderFocus |
P2452
|
FINISHED |
| Object | women |
—
|
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: women | Statement: [At the Noon Hour, subjectGenderFocus, women]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectGenderFocus Context triple: [At the Noon Hour, subjectGenderFocus, women]
-
A.
hasGenderFocus
chosen
Indicates that something is specifically concerned with, oriented toward, or primarily addressing a particular gender or gender-related issues.
-
B.
genderTarget
Indicates that an action, message, or effect is specifically directed toward entities of a particular gender.
-
C.
genderSpecificity
Indicates whether the relationship or action applies specifically to a particular gender or is gender-neutral.
-
D.
genderOfPersona
Indicates the gender identity associated with a given persona.
-
E.
genderImplication
Indicates that one entity’s gender suggests, constrains, or determines the possible or likely gender of another entity.
- 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_69eeb31b6dcc8190b30632dc3928a0c0 |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69f6640168948190811bd5f933a87cf5 |
completed | May 2, 2026, 8:52 p.m. |
| PD | Predicate disambiguation | batch_69f6633451948190bcc0410602bb4914 |
completed | May 2, 2026, 8:48 p.m. |
Created at: April 27, 2026, 1:22 a.m.