Triple

T20870501
Position Surface form Disambiguated ID Type / Status
Subject Hal Kanter E513877 entity
Predicate fullName P16 FINISHED
Object Harold Leonard Kanter
Harold Leonard Kanter was an American comedy writer, producer, and director best known for his work in radio, television, and film from the mid-20th century.
 E1643692 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: Harold Leonard Kanter | Statement: [Hal Kanter, fullName, Harold Leonard Kanter]
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: Harold Leonard Kanter
Triple: [Hal Kanter, fullName, Harold Leonard Kanter]
Generated description
Harold Leonard Kanter was an American comedy writer, producer, and director best known for his work in radio, television, and film from 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_69e0b4f675cc8190b4e745225b62eb66 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c4637ec48190830023d20fb8124c completed April 21, 2026, 12:27 a.m.
NED1 Entity disambiguation (via context triple) batch_6a100445cb34819088d202b46f537702 completed May 22, 2026, 7:22 a.m.
NEDg Description generation batch_6a1005d90a2481908a5eec89c050867b completed May 22, 2026, 7:29 a.m.
NED2 Entity disambiguation (via description) batch_6a100659e1048190928b7723ab5363ce completed May 22, 2026, 7:31 a.m.
Created at: April 16, 2026, 12:45 p.m.