Triple

T30642977
Position Surface form Disambiguated ID Type / Status
Subject William F. Nolan E780038 entity
Predicate notableFor P22 FINISHED
Object co-writing the novel Logan's Run
Co-writing the novel "Logan's Run" refers to William F. Nolan’s collaboration in creating the influential 1967 science fiction work that inspired several film and television adaptations.
 E1922908 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: co-writing the novel Logan's Run | Statement: [William F. Nolan, notableFor, co-writing the novel Logan's Run]
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: co-writing the novel Logan's Run
Triple: [William F. Nolan, notableFor, co-writing the novel Logan's Run]
Generated description
Co-writing the novel "Logan's Run" refers to William F. Nolan’s collaboration in creating the influential 1967 science fiction work that inspired several film and television adaptations.

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_69f224a50ebc81909b961a94c7f66b12 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f68a57cc808190830b6ca9b5b83468 completed May 2, 2026, 11:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2863f60ac881909650031b59a531cd completed June 9, 2026, 7:05 p.m.
NEDg Description generation batch_6a2864f21c548190a70918ec6ce828e4 completed June 9, 2026, 7:09 p.m.
NED2 Entity disambiguation (via description) batch_6a286566d4c08190891c25fc18cdd5d7 completed June 9, 2026, 7:11 p.m.
Created at: April 29, 2026, 8:29 p.m.