Triple

T27173501
Position Surface form Disambiguated ID Type / Status
Subject 2020 Democratic Party presidential primaries E682980 entity
Predicate hasMajorOpponent P69393 FINISHED
Object Michael Bennet
Michael Bennet is a U.S. Senator from Colorado and a moderate Democrat who briefly sought his party’s 2020 presidential nomination.
E1761156 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: Michael Bennet | Statement: [2020 Democratic Party presidential primaries, hasMajorOpponent, Michael Bennet]
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: Michael Bennet
Triple: [2020 Democratic Party presidential primaries, hasMajorOpponent, Michael Bennet]
Generated description
Michael Bennet is a U.S. Senator from Colorado and a moderate Democrat who briefly sought his party’s 2020 presidential nomination.

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_69eefad086808190ab89816c0c300476 completed April 27, 2026, 5:57 a.m.
NER Named-entity recognition batch_69fde1d15a2c8190a6beb3a2ce867f0c completed May 8, 2026, 1:14 p.m.
NED1 Entity disambiguation (via context triple) batch_6a125388d6a0819092971a404f7992c5 completed May 24, 2026, 1:25 a.m.
NEDg Description generation batch_6a12546f814881908c806a1805b7473d completed May 24, 2026, 1:29 a.m.
NED2 Entity disambiguation (via description) batch_6a12552837d88190a12496ca49423f0f completed May 24, 2026, 1:32 a.m.
Created at: April 27, 2026, 9:24 a.m.