Triple
T7560188
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Topa |
E178773
|
entity |
| Predicate | storylineTheme |
P76865
|
FINISHED |
| Object | gender identity |
—
|
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: gender identity | Statement: [Topa, storylineTheme, gender identity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: storylineTheme Context triple: [Topa, storylineTheme, gender identity]
-
A.
storyline
Indicates that one entity serves as the narrative plot or sequence of events associated with another entity.
-
B.
dramaticTheme
Indicates that a work, scene, or narrative centers around a particular dramatic subject, motif, or emotional conflict as its main thematic focus.
-
C.
theme
Indicates the entity that is the primary participant or content affected or characterized by an action, event, or state.
-
D.
hasThemeInStory
chosen
Indicates that a particular theme is present or plays a significant role within a given story.
-
E.
characterTheme
Indicates that a particular theme, motif, or conceptual focus is associated with a given character.
- 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_69c69f2da22c8190a50942ac20af70e8 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8dd96488190b4cca25ae8f7f95c |
completed | March 27, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69c6f4dc485c819080da13e3b7f4f08f |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:50 p.m.