Triple

T27854082
Position Surface form Disambiguated ID Type / Status
Subject Edgar Prince E704036 entity
Predicate notableWork P4 FINISHED
Object Prince Corporation
Prince Corporation was an automotive parts manufacturing company founded by businessman and engineer Edgar Prince, known for pioneering innovations in sun visors and other interior car components.
E1791234 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: Prince Corporation | Statement: [Edgar Prince, notableWork, Prince Corporation]
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: Prince Corporation
Triple: [Edgar Prince, notableWork, Prince Corporation]
Generated description
Prince Corporation was an automotive parts manufacturing company founded by businessman and engineer Edgar Prince, known for pioneering innovations in sun visors and other interior car components.

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_69ef840e614c8190a88cf9638c14a265 completed April 27, 2026, 3:43 p.m.
NER Named-entity recognition batch_69f6390779d8819081ea28fee196211d completed May 2, 2026, 5:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12f73da338819089f56c2cccce25d7 completed May 24, 2026, 1:03 p.m.
NEDg Description generation batch_6a12f7ff676c8190aee03de906240938 completed May 24, 2026, 1:07 p.m.
NED2 Entity disambiguation (via description) batch_6a12fb9bdbe881909c9f79d153f151a3 completed May 24, 2026, 1:22 p.m.
Created at: April 27, 2026, 6:13 p.m.