Triple
T29090795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Passing (novel) |
E734846
|
entity |
| Predicate | protagonistsSkinColor |
P138456
|
FINISHED |
| Object | light-skinned Black women |
—
|
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: light-skinned Black women | Statement: [Passing (novel), protagonistsSkinColor, light-skinned Black women]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: protagonistsSkinColor Context triple: [Passing (novel), protagonistsSkinColor, light-skinned Black women]
-
A.
protagonistSkinTone
chosen
Indicates that a character serves as the main protagonist and specifies the color or shade of their skin.
-
B.
protagonistEthnicity
Indicates the ethnic background or cultural heritage associated with a work’s main character.
-
C.
protagonistIs
Indicates that one entity serves as the main character or central figure in relation to another entity or narrative context.
-
D.
hasBlackProtagonist
Indicates that the work features a main character whose racial identity is Black.
-
E.
protagonistNationality
Indicates the country or national identity to which the protagonist of a work is associated or belongs.
- 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_69f05b0ed66481908f2e864fa550d2f1 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f6691f5e188190b12c7b2eb729a45e |
completed | May 2, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69f6659b62fc8190b21555d0ba54db2d |
completed | May 2, 2026, 8:59 p.m. |
Created at: April 28, 2026, 11:05 a.m.