Triple

T38171428
Position Surface form Disambiguated ID Type / Status
Subject Lionheart E1000084 entity
Predicate character P662 FINISHED
Object Lyon Gaultier
Lyon Gaultier is the main martial artist protagonist of the 1990 action film "Lionheart," portrayed by Jean-Claude Van Damme as a French Foreign Legionnaire who enters underground fights to support his late brother’s family.
E2259630 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: Lyon Gaultier | Statement: [Lionheart, character, Lyon Gaultier]
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: Lyon Gaultier
Triple: [Lionheart, character, Lyon Gaultier]
Generated description
Lyon Gaultier is the main martial artist protagonist of the 1990 action film "Lionheart," portrayed by Jean-Claude Van Damme as a French Foreign Legionnaire who enters underground fights to support his late brother’s family.

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_69f76daaace48190a38cee37f8ce343f completed May 3, 2026, 3:45 p.m.
NER Named-entity recognition batch_69fc4660e4bc81909ccc8feed391e8fe completed May 7, 2026, 7:59 a.m.
NED1 Entity disambiguation (via context triple) batch_6a417b2f59d08190acacb13faddd6271 completed June 28, 2026, 7:51 p.m.
NEDg Description generation batch_6a417dd5c4b48190a6630675b3952122 completed June 28, 2026, 8:02 p.m.
NED2 Entity disambiguation (via description) batch_6a417e4fbe288190a20979ce6399817a completed June 28, 2026, 8:04 p.m.
Created at: May 3, 2026, 4:29 p.m.