Triple

T31065303
Position Surface form Disambiguated ID Type / Status
Subject Forever Young E791649 entity
Predicate characterPortrayedBy P1507 FINISHED
Object Claire Cooper – Jamie Lee Curtis
Claire Cooper is the main female character in the romantic fantasy film "Forever Young," portrayed by actress Jamie Lee Curtis.
E186111 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: Claire Cooper – Jamie Lee Curtis | Statement: [Forever Young, characterPortrayedBy, Claire Cooper – Jamie Lee Curtis]
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: Claire Cooper – Jamie Lee Curtis
Triple: [Forever Young, characterPortrayedBy, Claire Cooper – Jamie Lee Curtis]
Generated description
Claire Cooper is the main female character in the romantic fantasy film "Forever Young," portrayed by actress Jamie Lee Curtis.

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_69f224cc0c5c81908404f087bff92997 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f69579eda48190b1bd6e7ce026b538 completed May 3, 2026, 12:23 a.m.
NED1 Entity disambiguation (via context triple) batch_6a292b1a17248190a7576ba5724e4684 completed June 10, 2026, 9:15 a.m.
NEDg Description generation batch_6a292dae6fc081908ca17d2de5465f6d completed June 10, 2026, 9:26 a.m.
NED2 Entity disambiguation (via description) batch_6a292e38a7bc81908dc2261b06212031 completed June 10, 2026, 9:28 a.m.
Created at: April 29, 2026, 9:01 p.m.