Triple

T33932868
Position Surface form Disambiguated ID Type / Status
Subject From Paris with Love E869946 entity
Predicate characterPortrayed P1507 FINISHED
Object John Travolta as Charlie Wax
John Travolta as Charlie Wax is a brash, unorthodox, and trigger-happy American spy whose over-the-top style drives the high-octane action and dark humor of the film.
 E2073347 NE 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: John Travolta as Charlie Wax | Statement: [From Paris with Love, characterPortrayed, John Travolta as Charlie Wax]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: John Travolta as Charlie Wax
Triple: [From Paris with Love, characterPortrayed, John Travolta as Charlie Wax]
Generated description
John Travolta as Charlie Wax is a brash, unorthodox, and trigger-happy American spy whose over-the-top style drives the high-octane action and dark humor of the film.

Provenance (5 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_69f3499a59788190bff762a891471b31 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f702294c488190a0ebe6b65ff1b551 completed May 3, 2026, 8:07 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36825a819481909cab43bd6c0acd65 completed June 20, 2026, 12:06 p.m.
NEDg Description generation batch_6a36831f01fc8190a5e2026f6039883d completed June 20, 2026, 12:10 p.m.
NED2 Entity disambiguation (via description) batch_6a36845c22bc819083a9cbe9c3f3be9a completed June 20, 2026, 12:15 p.m.
Created at: May 1, 2026, 1:49 a.m.