Triple
T4269030
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daughter of Shanghai |
E96893
|
entity |
| Predicate | hasHeroine |
P19972
|
FINISHED |
| Object | Chinese American woman |
—
|
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: Chinese American woman | Statement: [Daughter of Shanghai, hasHeroine, Chinese American woman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHeroine Context triple: [Daughter of Shanghai, hasHeroine, Chinese American woman]
-
A.
hasProtagonist
Indicates that a work of narrative has a main character who serves as its central focus or driving agent.
-
B.
hasFemaleEquivalent
Indicates that one entity serves as the female counterpart or equivalent of another entity.
-
C.
hasStrongFemaleCharacters
chosen
Indicates that the work features prominent, well-developed female characters who display agency, complexity, and significant influence on the narrative or outcome.
-
D.
hasFemaleSpeaker
Indicates that the associated content, event, or communication is spoken or narrated by a female individual.
-
E.
hasVillain
Indicates that one entity is the villain or primary antagonist associated with another entity.
- 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_69b34543f06c8190915ebb1a4574ffa9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34ff913608190b6ccf4a85057b07b |
completed | March 12, 2026, 11:44 p.m. |
| PD | Predicate disambiguation | batch_69b347f8dcb08190a725c1f7fb5a7466 |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:07 p.m.