Triple

T25364413
Position Surface form Disambiguated ID Type / Status
Subject Abbott E636059 entity
Predicate hasNotableBearer P458 FINISHED
Object Willis J. Abbott
Willis J. Abbott was an American journalist, editor, and author known for his work in newspapers and for writing popular histories and biographies in the late 19th and early 20th centuries.
 E2092854 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: Willis J. Abbott | Statement: [Abbott, hasNotableBearer, Willis J. Abbott]
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: Willis J. Abbott
Triple: [Abbott, hasNotableBearer, Willis J. Abbott]
Generated description
Willis J. Abbott was an American journalist, editor, and author known for his work in newspapers and for writing popular histories and biographies in the late 19th and early 20th centuries.

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_69e75a9b7cf481909f2dcdfb37d95ca7 completed April 21, 2026, 11:08 a.m.
NER Named-entity recognition batch_69f4a10ce1a8819081abc0012ddfaee7 completed May 1, 2026, 12:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3704765f208190a18f228355529365 completed June 20, 2026, 9:21 p.m.
NEDg Description generation batch_6a370577d8e08190848ce63a9865793d completed June 20, 2026, 9:26 p.m.
NED2 Entity disambiguation (via description) batch_6a3706295cc48190b7c3753311f24fa6 completed June 20, 2026, 9:29 p.m.
Created at: April 21, 2026, 1:36 p.m.