Triple
T15306122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Cider House Rules (1999 film) score |
E365901
|
entity |
| Predicate | composerGender |
P25113
|
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: [The Cider House Rules (1999 film) score, composerGender, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: composerGender Context triple: [The Cider House Rules (1999 film) score, composerGender, female]
-
A.
playsGender
Indicates that one entity performs or assumes a particular gender role or identity in a given context.
-
B.
creatorSexOrGender
chosen
Indicates that the specified sex or gender is the sex or gender of the creator of the referenced work or entity.
-
C.
namedForGender
Indicates that one entity is named in a way that reflects or is derived from a particular gender or gender-related characteristic of another entity.
-
D.
genderOfEponym
Indicates the gender of the person after whom something (such as a place, object, or concept) is named.
-
E.
plugGender
Indicates that one entity’s connector has a specified gender (e.g., male, female, neutral) in relation to another connector or interface.
- 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03ccef14c819099c5ebe962e7f867 |
completed | April 16, 2026, 1:35 a.m. |
| PD | Predicate disambiguation | batch_69deca935e2c8190b640987ddfc542b9 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:16 a.m.