Triple

T36341128
Position Surface form Disambiguated ID Type / Status
Subject You Can’t Cheat an Honest Man E894922 entity
Predicate screenwriter P2831 FINISHED
Object Richard Mack
Richard Mack was a screenwriter known for his work on classic American films, including contributing to the screenplay of the 1939 comedy "You Can’t Cheat an Honest Man."
E2180448 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: Richard Mack | Statement: [You Can’t Cheat an Honest Man, screenwriter, Richard Mack]
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: Richard Mack
Triple: [You Can’t Cheat an Honest Man, screenwriter, Richard Mack]
Generated description
Richard Mack was a screenwriter known for his work on classic American films, including contributing to the screenplay of the 1939 comedy "You Can’t Cheat an Honest Man."

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_69f76e4e90148190b02fe52593c70b5b completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7ba9d9b1c8190af0db84287050e6c completed May 3, 2026, 9:14 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39a31e69448190a4771e22bdd11906 completed June 22, 2026, 9:03 p.m.
NEDg Description generation batch_6a39a3e4a9488190a77ea18c86b5af48 completed June 22, 2026, 9:06 p.m.
NED2 Entity disambiguation (via description) batch_6a39a49278088190b10c591fec7c42e2 completed June 22, 2026, 9:09 p.m.
Created at: May 3, 2026, 4:09 p.m.