Triple
T34263325
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Torchy Runs for Mayor |
E879093
|
entity |
| Predicate | featuresStrongFemaleLead |
P19972
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Torchy Runs for Mayor, featuresStrongFemaleLead, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresStrongFemaleLead Context triple: [Torchy Runs for Mayor, featuresStrongFemaleLead, true]
-
A.
hasStrongFemaleCharacters
chosen
Indicates that the work features prominent, well-developed female characters who display agency, complexity, and significant influence on the narrative or outcome.
-
B.
femaleLeadCharacterStatus
Indicates the narrative or role status assigned to a female lead character within a story or production.
-
C.
numberOfMainFemaleLeadsInWork
Indicates the number of primary female lead characters that appear in a given work.
-
D.
relationshipTypeWithFemaleLead
Indicates the type or nature of a relationship that an entity has with a female lead.
-
E.
hasLeadCharacterGender
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_69f349b421cc8190b4b4655e1d612548 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a0311c202408190be88a85337aacf11 |
completed | May 12, 2026, 11:40 a.m. |
| PD | Predicate disambiguation | batch_6a0310b0c9c88190ab218d47d4f432ed |
completed | May 12, 2026, 11:36 a.m. |
Created at: May 1, 2026, 1:56 a.m.