Triple
T5694532
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anupam Kher as Dr. Cliff Patel |
E125504
|
entity |
| Predicate | portrayalInvolvesTheme |
P30025
|
FINISHED |
| Object | mentalHealth |
—
|
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: mentalHealth | Statement: [Anupam Kher as Dr. Cliff Patel, portrayalInvolvesTheme, mentalHealth]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayalInvolvesTheme Context triple: [Anupam Kher as Dr. Cliff Patel, portrayalInvolvesTheme, mentalHealth]
-
A.
portrayalFeature
Indicates that one entity serves as a characteristic, aspect, or attribute highlighted in the depiction or representation of another entity.
-
B.
followsInTheme
Indicates that one element continues or succeeds another while maintaining the same theme or thematic context.
-
C.
portrayalRecognition
Indicates that one entity recognizes or identifies another entity as a portrayal or representation of a particular subject or character.
-
D.
portrayalLedTo
Indicates that one entity’s portrayal of another caused or significantly contributed to a subsequent outcome, reaction, or state involving that other entity.
-
E.
themeInvolvingCharacter
chosen
Indicates that a theme, motif, or abstract concept centrally involves or is significantly shaped by a particular 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_69c0082bb19c8190823a4facd3cba79b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c029014588819094a2a0f6f9b66bab |
completed | March 22, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69c021c0e0408190ab6c3cd3f907e80f |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:45 p.m.