Triple

T26480373
Position Surface form Disambiguated ID Type / Status
Subject Michael Henry Brown E664664 entity
Predicate notableWork P4 FINISHED
Object In Too Deep
In Too Deep is a 1999 American crime thriller film about an undercover cop infiltrating a powerful drug lord’s operation, written by Michael Henry Brown.
 E163929 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: In Too Deep | Statement: [Michael Henry Brown, notableWork, In Too Deep]
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: In Too Deep
Triple: [Michael Henry Brown, notableWork, In Too Deep]
Generated description
In Too Deep is a 1999 American crime thriller film about an undercover cop infiltrating a powerful drug lord’s operation, written by Michael Henry Brown.

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_69ee883bc85481909885f92415cbce33 completed April 26, 2026, 9:48 p.m.
NER Named-entity recognition batch_69f612fa3f4481908d1db59213f734d2 completed May 2, 2026, 3:06 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11c80630a88190ac56222d63f7d06a completed May 23, 2026, 3:30 p.m.
NEDg Description generation batch_6a11c919c3d08190ae5cc3a21f5257be completed May 23, 2026, 3:34 p.m.
NED2 Entity disambiguation (via description) batch_6a11ca2243988190a158631f4b94e205 completed May 23, 2026, 3:39 p.m.
Created at: April 27, 2026, 12:26 a.m.