Triple

T29428677
Position Surface form Disambiguated ID Type / Status
Subject The Secrets We Keep E746363 entity
Predicate featuresCharacter P626 FINISHED
Object Thomas
Thomas is a central character in the psychological thriller film "The Secrets We Keep," which explores themes of trauma, revenge, and moral ambiguity in the aftermath of World War II.
E1865719 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: Thomas | Statement: [The Secrets We Keep, featuresCharacter, Thomas]
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: Thomas
Triple: [The Secrets We Keep, featuresCharacter, Thomas]
Generated description
Thomas is a central character in the psychological thriller film "The Secrets We Keep," which explores themes of trauma, revenge, and moral ambiguity in the aftermath of World War II.

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_69f0a7a06e0081908add494075912eb4 completed April 28, 2026, 12:27 p.m.
NER Named-entity recognition batch_69f66ac7f69c81909b76956066b0fa8d completed May 2, 2026, 9:21 p.m.
NED1 Entity disambiguation (via context triple) batch_6a25d90751688190bc0e6453ae394d23 completed June 7, 2026, 8:48 p.m.
NEDg Description generation batch_6a25de1bdc688190abf553027c827d31 completed June 7, 2026, 9:09 p.m.
NED2 Entity disambiguation (via description) batch_6a25de76e5188190987657d06a833524 completed June 7, 2026, 9:11 p.m.
Created at: April 28, 2026, 3:11 p.m.