Triple

T37232052
Position Surface form Disambiguated ID Type / Status
Subject Ohio Gang corruption scandals E923159 entity
Predicate participant P858 FINISHED
Object Thomas W. Miller
Thomas W. Miller was an American politician and public official best known for his role in the Harding-era Ohio Gang and his subsequent conviction for corruption.
E2295636 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: Thomas W. Miller | Statement: [Ohio Gang corruption scandals, participant, Thomas W. Miller]
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: Thomas W. Miller
Triple: [Ohio Gang corruption scandals, participant, Thomas W. Miller]
Generated description
Thomas W. Miller was an American politician and public official best known for his role in the Harding-era Ohio Gang and his subsequent conviction for corruption.

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_69f76ea7f0008190b31b8e30f3d05a71 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fb36cc34ec8190ac4fc59c31d942bd completed May 6, 2026, 12:40 p.m.
NED1 Entity disambiguation (via context triple) batch_6a81cf1defa88190931de03f9a3a2d8f completed Aug. 16, 2026, 2:54 p.m.
NEDg Description generation batch_6a81cf8aa6c481908e67c74c1d17ff93 completed Aug. 16, 2026, 2:56 p.m.
NED2 Entity disambiguation (via description) batch_6a81d121cad88190b91091f19507ca01 completed Aug. 16, 2026, 3:02 p.m.
Created at: May 3, 2026, 4:15 p.m.