Triple

T38438890
Position Surface form Disambiguated ID Type / Status
Subject Rabbit Test E906430 entity
Predicate productionCompany P490 FINISHED
Object Edgar Scherick Associates
Edgar Scherick Associates was a film and television production company founded by producer Edgar J. Scherick, known for backing various feature films and TV projects in the 1970s and 1980s.
E2270079 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: Edgar Scherick Associates | Statement: [Rabbit Test, productionCompany, Edgar Scherick Associates]
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: Edgar Scherick Associates
Triple: [Rabbit Test, productionCompany, Edgar Scherick Associates]
Generated description
Edgar Scherick Associates was a film and television production company founded by producer Edgar J. Scherick, known for backing various feature films and TV projects in the 1970s and 1980s.

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_69f76e72878c8190a692836c8b01b58b completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fccdd496048190bca801a8a9eecb62 completed May 7, 2026, 5:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41c2990f788190b026d958118fff53 completed June 29, 2026, 12:55 a.m.
NEDg Description generation batch_6a41c3c4ef1c8190a88b7bf1a2b782fa completed June 29, 2026, 1 a.m.
NED2 Entity disambiguation (via description) batch_6a41c6066520819087dbfcda0751628b completed June 29, 2026, 1:10 a.m.
Created at: May 3, 2026, 4:31 p.m.