Triple

T23768630
Position Surface form Disambiguated ID Type / Status
Subject Stephanie Zimbalist E587456 entity
Predicate appearedIn P795 FINISHED
Object The Man in the Brown Suit
The Man in the Brown Suit is a television film adaptation of Agatha Christie's mystery novel, involving a young woman entangled in murder and intrigue after witnessing a fatal accident.
 E1600185 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: The Man in the Brown Suit | Statement: [Stephanie Zimbalist, appearedIn, The Man in the Brown Suit]
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: The Man in the Brown Suit
Triple: [Stephanie Zimbalist, appearedIn, The Man in the Brown Suit]
Generated description
The Man in the Brown Suit is a television film adaptation of Agatha Christie's mystery novel, involving a young woman entangled in murder and intrigue after witnessing a fatal accident.

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_69e2490b8ac48190a6b35f1d5500486b completed April 17, 2026, 2:51 p.m.
NER Named-entity recognition batch_69f1c4638b248190b512841493778483 completed April 29, 2026, 8:42 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f53e1fb9081908903ecf7d81bbf40 completed May 21, 2026, 6:50 p.m.
NEDg Description generation batch_6a0f555a0a5c819089d26a13abebd43a completed May 21, 2026, 6:56 p.m.
NED2 Entity disambiguation (via description) batch_6a0f57ca769081908f3ae56eeb36bfba completed May 21, 2026, 7:06 p.m.
Created at: April 17, 2026, 7:15 p.m.