Triple

T32017458
Position Surface form Disambiguated ID Type / Status
Subject Henry Singleton E817583 entity
Predicate name P16 FINISHED
Object Henry Earl Singleton
Henry Earl Singleton was an American engineer, businessman, and co-founder of Teledyne who became renowned as one of the most successful and unconventional capital allocators in corporate history.
E1989704 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: Henry Earl Singleton | Statement: [Henry Singleton, name, Henry Earl Singleton]
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: Henry Earl Singleton
Triple: [Henry Singleton, name, Henry Earl Singleton]
Generated description
Henry Earl Singleton was an American engineer, businessman, and co-founder of Teledyne who became renowned as one of the most successful and unconventional capital allocators in corporate history.

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_69f348f9e5d081908cc3f57c4942af52 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6b43bac9881908cc2bfe063135df1 completed May 3, 2026, 2:34 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2ed4e74c20819086ff8e4086dd70bd completed June 14, 2026, 4:20 p.m.
NEDg Description generation batch_6a2ed614bb04819081528a6e61d52a73 completed June 14, 2026, 4:25 p.m.
NED2 Entity disambiguation (via description) batch_6a2ed800b70c8190a2776741e12710b9 completed June 14, 2026, 4:34 p.m.
Created at: May 1, 2026, 12:16 a.m.