Triple

T25621741
Position Surface form Disambiguated ID Type / Status
Subject Burlington Industries, Inc. E642315 entity
Predicate competitor P1375 FINISHED
Object J.P. Stevens & Co.
J.P. Stevens & Co. was a major American textile manufacturing company that played a prominent role in the U.S. textile industry and in landmark labor and employment disputes in the 20th century.
 E1687604 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: J.P. Stevens & Co. | Statement: [Burlington Industries, Inc., competitor, J.P. Stevens & Co.]
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: J.P. Stevens & Co.
Triple: [Burlington Industries, Inc., competitor, J.P. Stevens & Co.]
Generated description
J.P. Stevens & Co. was a major American textile manufacturing company that played a prominent role in the U.S. textile industry and in landmark labor and employment disputes in the 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_69e77e7a96748190b10f2699041e4e43 completed April 21, 2026, 1:41 p.m.
NER Named-entity recognition batch_69f5fa2121f4819082c5135147a0f0d9 completed May 2, 2026, 1:20 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10b77ef9248190a52c7e8c5ab12a4e completed May 22, 2026, 8:07 p.m.
NEDg Description generation batch_6a10b8271a108190b42828d33fef380e completed May 22, 2026, 8:10 p.m.
NED2 Entity disambiguation (via description) batch_6a10b9606818819094491a74c5922378 completed May 22, 2026, 8:15 p.m.
Created at: April 21, 2026, 5:05 p.m.