Triple

T33940456
Position Surface form Disambiguated ID Type / Status
Subject Monitor E870148 entity
Predicate hasHost P2592 FINISHED
Object John Bartholomew Tucker
John Bartholomew Tucker was an American television and radio personality best known for hosting game shows and talk programs during the mid-20th century.
 E2074714 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: John Bartholomew Tucker | Statement: [Monitor, hasHost, John Bartholomew Tucker]
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: John Bartholomew Tucker
Triple: [Monitor, hasHost, John Bartholomew Tucker]
Generated description
John Bartholomew Tucker was an American television and radio personality best known for hosting game shows and talk programs during the mid-20th century.

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_69f3499b0dd48190b07b4b60babcee02 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f70235d7dc8190bbd64b887ba633ed completed May 3, 2026, 8:07 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3689d58cf08190ae6283265087d590 completed June 20, 2026, 12:38 p.m.
NEDg Description generation batch_6a368a5f070c81909a5d0e8f4ac5ad2e completed June 20, 2026, 12:41 p.m.
NED2 Entity disambiguation (via description) batch_6a368b17fd848190be803db49a0ef089 completed June 20, 2026, 12:44 p.m.
Created at: May 1, 2026, 1:49 a.m.