Triple
T9885130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Licence to Kill |
E180910
|
entity |
| Predicate | featuresPortrayalOf |
P49090
|
FINISHED |
| Object | revenge |
—
|
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: revenge | Statement: [Licence to Kill, featuresPortrayalOf, revenge]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresPortrayalOf Context triple: [Licence to Kill, featuresPortrayalOf, revenge]
-
A.
portrayalFeature
chosen
Indicates that one entity serves as a characteristic, aspect, or attribute highlighted in the depiction or representation of another entity.
-
B.
featuresFigureOf
Indicates that one entity includes or presents another entity as a figure, illustration, or visual element.
-
C.
featuresIn
Indicates that an entity appears or plays a role within another entity, such as a person or element being included in a work, event, or context.
-
D.
featuresText
Indicates that an entity includes or presents a specific piece of text as one of its characteristics or contents.
-
E.
featuresDecor
Indicates that one entity includes or showcases the decor elements provided or defined by another entity.
- 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_69ca828082cc8190a40f8d299caa6545 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cdb45549488190833200977d558e47 |
completed | April 2, 2026, 12:12 a.m. |
| PD | Predicate disambiguation | batch_69cd1d810ed48190a252b70e9390c8f3 |
completed | April 1, 2026, 1:28 p.m. |
Created at: March 30, 2026, 8:38 p.m.