Triple

T28253140
Position Surface form Disambiguated ID Type / Status
Subject Baldwin political family of Connecticut E712368 entity
Predicate hasNotableMember P304 FINISHED
Object Raymond E. Baldwin
Raymond E. Baldwin was a prominent 20th-century American politician who served as both governor of Connecticut and a U.S. senator.
 E2297683 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: Raymond E. Baldwin | Statement: [Baldwin political family of Connecticut, hasNotableMember, Raymond E. Baldwin]
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: Raymond E. Baldwin
Triple: [Baldwin political family of Connecticut, hasNotableMember, Raymond E. Baldwin]
Generated description
Raymond E. Baldwin was a prominent 20th-century American politician who served as both governor of Connecticut and a U.S. senator.

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_69efb5207eb08190827e4c34048030b1 completed April 27, 2026, 7:12 p.m.
NER Named-entity recognition batch_69f643f207048190b056120fcb9a5e7c completed May 2, 2026, 6:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a83c2bca2008190af4e1216e062a4eb completed Aug. 18, 2026, 2:26 a.m.
NEDg Description generation batch_6a83c3bdd21c81908e617da0460af6c9 completed Aug. 18, 2026, 2:30 a.m.
NED2 Entity disambiguation (via description) batch_6a83c40d6fb48190b1453b127cebd5e7 completed Aug. 18, 2026, 2:31 a.m.
Created at: April 27, 2026, 11:06 p.m.