Triple
T16164534
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tanya Roberts as Stacey Sutton |
E392266
|
entity |
| Predicate | characterGenderInFiction |
P92864
|
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: [Tanya Roberts as Stacey Sutton, characterGenderInFiction, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterGenderInFiction Context triple: [Tanya Roberts as Stacey Sutton, characterGenderInFiction, female]
-
A.
fictionalGender
chosen
Indicates that one entity has a gender identity or classification that exists only within a fictional or imaginary context.
-
B.
genderDepicted
Indicates that the relationship specifies the gender of the entity as it is represented or portrayed in some context.
-
C.
featuredGender
Indicates that a particular gender is highlighted, emphasized, or given primary focus in a given context or presentation.
-
D.
genderOfPersona
Indicates the gender identity associated with a given persona.
-
E.
genderOfPseudonym
Indicates the gender associated with a given pseudonym or pen name.
- 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_69d87f1d32208190942e4e499a80c18c |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21e622ae481909f3cf25b38886d3a |
completed | April 17, 2026, 11:49 a.m. |
| PD | Predicate disambiguation | batch_69e1828abb608190a99d86bce1d77de2 |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 5:02 a.m.