Triple
T36133270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Man Who Turned White |
E1045082
|
entity |
| Predicate | hasLeadCharacterEthnicityTheme |
P28254
|
FINISHED |
| Object | Asian characters in Western society |
—
|
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: Asian characters in Western society | Statement: [The Man Who Turned White, hasLeadCharacterEthnicityTheme, Asian characters in Western society]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLeadCharacterEthnicityTheme Context triple: [The Man Who Turned White, hasLeadCharacterEthnicityTheme, Asian characters in Western society]
-
A.
hasRacialTheme
Indicates that something contains or centers on themes, issues, or representations related to race or racial identity.
-
B.
leadActorEthnicity
Indicates the ethnic background or identity of the primary (lead) actor in a work.
-
C.
hasEthnicCharacteristic
Indicates that an entity possesses or is associated with a particular ethnic characteristic or identity.
-
D.
protagonistEthnicity
chosen
Indicates the ethnic background or cultural heritage associated with a work’s main character.
-
E.
portrayedByEthnicity
Indicates that an entity is portrayed or represented by someone of a specified ethnic background.
- 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_69f76e36a4508190b5bfc8f594272a4c |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fe5ec9028081909ae3d6fbe2f4cbbc |
completed | May 8, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69fe5e1d715881909fc516fafc707644 |
completed | May 8, 2026, 10:05 p.m. |
Created at: May 3, 2026, 4:08 p.m.