Triple

T24597400
Position Surface form Disambiguated ID Type / Status
Subject No Man of Her Own (1932 film) E608717 entity
Predicate basedOn P98 FINISHED
Object No Man of Her Own by Val Lewton
"No Man of Her Own" by Val Lewton is a novel that served as the literary source for the 1932 romantic drama film of the same name.
 E1641993 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: No Man of Her Own by Val Lewton | Statement: [No Man of Her Own (1932 film), basedOn, No Man of Her Own by Val Lewton]
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: No Man of Her Own by Val Lewton
Triple: [No Man of Her Own (1932 film), basedOn, No Man of Her Own by Val Lewton]
Generated description
"No Man of Her Own" by Val Lewton is a novel that served as the literary source for the 1932 romantic drama film 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_69e2c4cf54248190af7b0c2d9ade9830 completed April 17, 2026, 11:39 p.m.
NER Named-entity recognition batch_69f2a9e04bdc81908b56a7c3f92ab346 completed April 30, 2026, 1:01 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0ff8846ee481908d82e2c5f2dd8cf9 completed May 22, 2026, 6:32 a.m.
NEDg Description generation batch_6a0ff96d423881908d81db0b6bc78921 completed May 22, 2026, 6:36 a.m.
NED2 Entity disambiguation (via description) batch_6a0ffa8bb5208190b473d834c40e7ef3 completed May 22, 2026, 6:41 a.m.
Created at: April 18, 2026, 2:30 a.m.