Triple
T8867202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Claire de Loone |
E211047
|
entity |
| Predicate | numberOfMainFemaleLeadsInWork |
P85078
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Claire de Loone, numberOfMainFemaleLeadsInWork, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMainFemaleLeadsInWork Context triple: [Claire de Loone, numberOfMainFemaleLeadsInWork, 3]
-
A.
hasStrongFemaleCharacters
Indicates that the work features prominent, well-developed female characters who display agency, complexity, and significant influence on the narrative or outcome.
-
B.
hasLeadCharacterGender
Indicates that the primary or lead character in a work has a specified gender.
-
C.
leadActress
Indicates that the subject is the primary female performer in the specified film, show, or production.
-
D.
numberOfHumanProtagonists
Indicates the count of human characters that serve as protagonists in a given work or context.
-
E.
hasFemaleLeader
Indicates that the subject entity is led or governed by a woman in a primary leadership role.
- F. None of above. chosen
Provenance (4 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_69ca838d3c7c8190a849566d5afd2b11 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6108530c819084559f4de669ce20 |
completed | April 1, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69cc5c279ea481908c71756f694b66bf |
completed | March 31, 2026, 11:43 p.m. |
| PDg | Predicate description generation | batch_69cc5cffe8ec819084c12770fe0578f2 |
completed | March 31, 2026, 11:47 p.m. |
Created at: March 30, 2026, 6:51 p.m.