Triple

T27515633
Position Surface form Disambiguated ID Type / Status
Subject Clyde Bruckman E694549 entity
Predicate notableWork P4 FINISHED
Object Man on the Flying Trapeze
Man on the Flying Trapeze is a 1935 American comedy film starring W.C. Fields, known for its blend of domestic farce and slapstick humor.
 E1775179 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: Man on the Flying Trapeze | Statement: [Clyde Bruckman, notableWork, Man on the Flying Trapeze]
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: Man on the Flying Trapeze
Triple: [Clyde Bruckman, notableWork, Man on the Flying Trapeze]
Generated description
Man on the Flying Trapeze is a 1935 American comedy film starring W.C. Fields, known for its blend of domestic farce and slapstick humor.

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_69ef53842afc8190ba6bd4e4999bda67 completed April 27, 2026, 12:16 p.m.
NER Named-entity recognition batch_69f62efcdc40819097bcda0390571b2f completed May 2, 2026, 5:06 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12bc01803c81909c5de50d94d2ac9e completed May 24, 2026, 8:51 a.m.
NEDg Description generation batch_6a12bc771b0481909cca1c87c805f0de completed May 24, 2026, 8:53 a.m.
NED2 Entity disambiguation (via description) batch_6a12bd38f1948190a0b1f05ff28d8289 completed May 24, 2026, 8:56 a.m.
Created at: April 27, 2026, 1:18 p.m.