Triple
T1847503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sydney Greenstreet |
E41316
|
entity |
| Predicate | oftenPortrayed |
P18297
|
FINISHED |
| Object | villains |
—
|
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: villains | Statement: [Sydney Greenstreet, oftenPortrayed, villains]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oftenPortrayed Context triple: [Sydney Greenstreet, oftenPortrayed, villains]
-
A.
oftenDepictedAs
chosen
Indicates that one entity is frequently represented or portrayed in the form, appearance, or symbolism of another entity.
-
B.
oftenContrastedWith
Indicates that one entity is frequently compared to another in a way that highlights their differences or opposing characteristics.
-
C.
challengesPortrayalOf
Indicates that one entity questions, disputes, or undermines the way another entity is represented or depicted.
-
D.
portraysAgeGroup
Indicates that one entity depicts or represents another entity as belonging to a particular age group.
-
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_69a88648cd44819093303206d96d76ad |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb32d35508190bf1c487dffbecaf0 |
completed | March 7, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69abafdca6d8819083c66f3a29fd9fd1 |
completed | March 7, 2026, 4:55 a.m. |
Created at: March 4, 2026, 7:33 p.m.