Triple
T5861689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stories for Boys |
E130288
|
entity |
| Predicate | typicalProtagonistGender |
P21355
|
FINISHED |
| Object | male |
—
|
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: male | Statement: [Stories for Boys, typicalProtagonistGender, male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalProtagonistGender Context triple: [Stories for Boys, typicalProtagonistGender, male]
-
A.
protagonistGenderIdentity
Indicates the gender identity attributed to or expressed by the protagonist in a given context.
-
B.
protagonistType
Indicates the role or category that the main character (protagonist) of a story or scenario belongs to.
-
C.
hasTypicalGenderAssociation
Indicates that one entity is commonly or culturally associated with a particular gender more than with other genders.
-
D.
featuredGender
Indicates that a particular gender is highlighted, emphasized, or given primary focus in a given context or presentation.
-
E.
hasLeadCharacterGender
chosen
Indicates that the primary or lead character in a work has a specified 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_69c0084f3bb08190a7720f55f7aa4252 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c044ab0a048190b84be40fb13c0f50 |
completed | March 22, 2026, 7:36 p.m. |
| PD | Predicate disambiguation | batch_69c03345ca0c819081c81148d054fed2 |
completed | March 22, 2026, 6:21 p.m. |
Created at: March 22, 2026, 3:56 p.m.