Triple

T30652138
Position Surface form Disambiguated ID Type / Status
Subject Okemos High School E780288 entity
Predicate notableAlumnus P304 FINISHED
Object James Caan (attended; did not graduate)
James Caan (attended; did not graduate) is an American actor best known for his roles in films such as "The Godfather," "Misery," and "Elf."
E1924815 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: James Caan (attended; did not graduate) | Statement: [Okemos High School, notableAlumnus, James Caan (attended; did not graduate)]
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: James Caan (attended; did not graduate)
Triple: [Okemos High School, notableAlumnus, James Caan (attended; did not graduate)]
Generated description
James Caan (attended; did not graduate) is an American actor best known for his roles in films such as "The Godfather," "Misery," and "Elf."

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_69f224a5d2b481908a6853cd0138e2d7 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f68a98249081909b4be467f5a37110 completed May 2, 2026, 11:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2863fd719081908e2342d6a071e05b completed June 9, 2026, 7:05 p.m.
NEDg Description generation batch_6a28684a404c8190a962f21531e8c643 completed June 9, 2026, 7:23 p.m.
NED2 Entity disambiguation (via description) batch_6a2868da530081908ae24444bc7ccca6 completed June 9, 2026, 7:26 p.m.
Created at: April 29, 2026, 8:30 p.m.