Triple
T12997910
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tate Langdon |
E322089
|
entity |
| Predicate | portrayalTone |
P49759
|
FINISHED |
| Object | sympathetic villain |
—
|
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: sympathetic villain | Statement: [Tate Langdon, portrayalTone, sympathetic villain]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayalTone Context triple: [Tate Langdon, portrayalTone, sympathetic villain]
-
A.
portrayalLanguage
Indicates the language in which something is depicted, represented, or expressed.
-
B.
portrayalFeature
Indicates that one entity serves as a characteristic, aspect, or attribute highlighted in the depiction or representation of another entity.
-
C.
portrayalReception
Indicates how a particular portrayal of someone or something is received, evaluated, or responded to by an audience or observers.
-
D.
contributesToTone
chosen
Indicates that one entity plays a role in shaping, influencing, or determining the overall tone or mood of another entity.
-
E.
portrayalRecognition
Indicates that one entity recognizes or identifies another entity as a portrayal or representation of a particular subject or 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_69d8076479b8819090afce3591939cdf |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e7980288190a9fe629a8cc76a52 |
completed | April 10, 2026, 10:49 p.m. |
| PD | Predicate disambiguation | batch_69d97dc153a081909d13a694993f074a |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 8:46 p.m.