Triple

T30430849
Position Surface form Disambiguated ID Type / Status
Subject Michael Polish E774159 entity
Predicate notableWork P4 FINISHED
Object 90 Minutes in Heaven
90 Minutes in Heaven is a faith-based drama film about a pastor’s near-death experience and spiritual journey, directed by Michael Polish and adapted from the bestselling book of the same name.
E1916000 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: 90 Minutes in Heaven | Statement: [Michael Polish, notableWork, 90 Minutes in Heaven]
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: 90 Minutes in Heaven
Triple: [Michael Polish, notableWork, 90 Minutes in Heaven]
Generated description
90 Minutes in Heaven is a faith-based drama film about a pastor’s near-death experience and spiritual journey, directed by Michael Polish and adapted from the bestselling book of the same name.

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_69f22492d2a88190995ce8745d9becaa completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f6866e2fcc819090808635c0818ed9 completed May 2, 2026, 11:19 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2798ba2cd48190b7d61c30747e94d7 completed June 9, 2026, 4:38 a.m.
NEDg Description generation batch_6a279a78c9b081909c2958bf8fa6968b completed June 9, 2026, 4:45 a.m.
NED2 Entity disambiguation (via description) batch_6a279befd2848190b904461897fd7801 completed June 9, 2026, 4:51 a.m.
Created at: April 29, 2026, 8:06 p.m.