Triple

T32994581
Position Surface form Disambiguated ID Type / Status
Subject Barbara Read E844189 entity
Predicate notableWork P4 FINISHED
Object The Man Who Cried Wolf
The Man Who Cried Wolf is a 1937 American mystery film featuring Barbara Read in a prominent role.
 E2032209 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 Who Cried Wolf | Statement: [Barbara Read, notableWork, The Man Who Cried Wolf]
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 Who Cried Wolf
Triple: [Barbara Read, notableWork, The Man Who Cried Wolf]
Generated description
The Man Who Cried Wolf is a 1937 American mystery film featuring Barbara Read in a prominent role.

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_69f3494d99988190b502c68926af2c4d completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d2166d3c8190b3d64ed1d3fd5bb5 completed May 3, 2026, 4:41 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34dac7221081909818658217a25311 completed June 19, 2026, 5:59 a.m.
NEDg Description generation batch_6a34dbb1e474819095ca57b4364327cd completed June 19, 2026, 6:03 a.m.
NED2 Entity disambiguation (via description) batch_6a34dc4513c48190993300ccc4c2a6d4 completed June 19, 2026, 6:05 a.m.
Created at: May 1, 2026, 1:22 a.m.