Triple

T26168968
Position Surface form Disambiguated ID Type / Status
Subject Ironton, Ohio, United States E654341 entity
Predicate foundedBy P104 FINISHED
Object John Campbell
John Campbell was a 19th-century industrialist and iron manufacturer credited with helping establish the iron industry and founding the city of Ironton in southern Ohio.
E1715875 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 Campbell | Statement: [Ironton, Ohio, United States, foundedBy, John Campbell]
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 Campbell
Triple: [Ironton, Ohio, United States, foundedBy, John Campbell]
Generated description
John Campbell was a 19th-century industrialist and iron manufacturer credited with helping establish the iron industry and founding the city of Ironton in southern Ohio.

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_69ee5b44391c81908bdbd8813ba9aa99 completed April 26, 2026, 6:36 p.m.
NER Named-entity recognition batch_69f60c4132a88190ba74c1c290cbe35d completed May 2, 2026, 2:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1185743a3081909531ca905478877b completed May 23, 2026, 10:46 a.m.
NEDg Description generation batch_6a11868d16508190ae8827d4b07f0192 completed May 23, 2026, 10:50 a.m.
NED2 Entity disambiguation (via description) batch_6a11876410f4819091b6b45abd657f54 completed May 23, 2026, 10:54 a.m.
Created at: April 26, 2026, 8:34 p.m.