Triple

T25042858
Position Surface form Disambiguated ID Type / Status
Subject And Then There Were None (1945 film) E627157 entity
Predicate castMember P1668 FINISHED
Object Harry Thurston
Harry Thurston was a film actor known for appearing in mid-20th-century movies, including the 1945 adaptation of Agatha Christie’s "And Then There Were None."
E1706527 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: Harry Thurston | Statement: [And Then There Were None (1945 film), castMember, Harry Thurston]
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: Harry Thurston
Triple: [And Then There Were None (1945 film), castMember, Harry Thurston]
Generated description
Harry Thurston was a film actor known for appearing in mid-20th-century movies, including the 1945 adaptation of Agatha Christie’s "And Then There Were None."

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_69e2ff2b4c80819087c916b2b16241b9 completed April 18, 2026, 3:48 a.m.
NER Named-entity recognition batch_69f4530d80148190b959bb48ff7e0c2f completed May 1, 2026, 7:15 a.m.
NED1 Entity disambiguation (via context triple) batch_6a111ade2a1c8190a1c5e79009f89664 completed May 23, 2026, 3:11 a.m.
NEDg Description generation batch_6a111b8972c8819098e58a3403b7dec9 completed May 23, 2026, 3:14 a.m.
NED2 Entity disambiguation (via description) batch_6a111c84387481909caa6fbfbb38d855 completed May 23, 2026, 3:18 a.m.
Created at: April 18, 2026, 6:08 a.m.