Triple
T9001932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oreshura |
E215056
|
entity |
| Predicate | femaleLeadTrait |
P78623
|
FINISHED |
| Object | popular girl at school |
—
|
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: popular girl at school | Statement: [Oreshura, femaleLeadTrait, popular girl at school]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: femaleLeadTrait Context triple: [Oreshura, femaleLeadTrait, popular girl at school]
-
A.
femaleFeature
chosen
Indicates that the subject possesses a characteristic or attribute that is typically associated with females.
-
B.
hasLeadCharacterGender
Indicates that the primary or lead character in a work has a specified gender.
-
C.
femaleHas
Indicates that a specified entity is female or possesses a female gender attribute in relation to another entity or context.
-
D.
numberOfMainFemaleLeadsInWork
Indicates the number of primary female lead characters that appear in a given work.
-
E.
hasStrongFemaleCharacters
Indicates that the work features prominent, well-developed female characters who display agency, complexity, and significant influence on the narrative or outcome.
- 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_69ca83a12d648190b1e4fe11e8a31890 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6956a6e08190bd3853a7c1c130eb |
completed | April 1, 2026, 12:39 a.m. |
| PD | Predicate disambiguation | batch_69cc5edd6cb48190b4fc6d6ca0418056 |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 7:05 p.m.