Triple

T25355902
Position Surface form Disambiguated ID Type / Status
Subject Tom Leahy E635818 entity
Predicate hasNameInstance P164167 FINISHED
Object Tom Leahy (television host)
Tom Leahy (television host) was an American TV personality best known for hosting the Wichita, Kansas children’s horror show “The Host and Rodney” in the 1960s.
E1674667 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: Tom Leahy (television host) | Statement: [Tom Leahy, hasNameInstance, Tom Leahy (television host)]
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: Tom Leahy (television host)
Triple: [Tom Leahy, hasNameInstance, Tom Leahy (television host)]
Generated description
Tom Leahy (television host) was an American TV personality best known for hosting the Wichita, Kansas children’s horror show “The Host and Rodney” in the 1960s.

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_69e75a9b7cf481909f2dcdfb37d95ca7 completed April 21, 2026, 11:08 a.m.
NER Named-entity recognition batch_69f64cb2b5f4819092e363d5076cddbb completed May 2, 2026, 7:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1075fd4c0081908b40904f881ea3b6 completed May 22, 2026, 3:27 p.m.
NEDg Description generation batch_6a1076ba66588190bc35f122ee016bb0 completed May 22, 2026, 3:31 p.m.
NED2 Entity disambiguation (via description) batch_6a1077935d948190b863fda3fdd47965 completed May 22, 2026, 3:34 p.m.
Created at: April 21, 2026, 1:36 p.m.