Triple

T34230783
Position Surface form Disambiguated ID Type / Status
Subject Surveyor General of New York E878184 entity
Predicate positionHeldBy P8 FINISHED
Object Van Rensselaer Richmond
Van Rensselaer Richmond was a 19th-century American civil engineer and politician from New York who served in key state offices and contributed to the development of the state's infrastructure.
E2087893 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: Van Rensselaer Richmond | Statement: [Surveyor General of New York, positionHeldBy, Van Rensselaer Richmond]
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: Van Rensselaer Richmond
Triple: [Surveyor General of New York, positionHeldBy, Van Rensselaer Richmond]
Generated description
Van Rensselaer Richmond was a 19th-century American civil engineer and politician from New York who served in key state offices and contributed to the development of the state's infrastructure.

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_69f349b16d0481908754e3069f05e0c1 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f710b0a19881908a0da016236a3872 completed May 3, 2026, 9:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36d5e29b848190a88f4935b113ef02 completed June 20, 2026, 6:03 p.m.
NEDg Description generation batch_6a36d94c882481908650f76a661a5c9f completed June 20, 2026, 6:17 p.m.
NED2 Entity disambiguation (via description) batch_6a36d9af4c6481909caec87ad9da5f8e completed June 20, 2026, 6:19 p.m.
Created at: May 1, 2026, 1:56 a.m.