Triple

T27154663
Position Surface form Disambiguated ID Type / Status
Subject Schieffelin family E682487 entity
Predicate hasNotableMember P304 FINISHED
Object Jacob Schieffelin
Jacob Schieffelin was an 18th–19th century American merchant and landowner from the prominent Schieffelin family, known for his role in early New York commerce and real estate development.
 E1758023 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: Jacob Schieffelin | Statement: [Schieffelin family, hasNotableMember, Jacob Schieffelin]
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: Jacob Schieffelin
Triple: [Schieffelin family, hasNotableMember, Jacob Schieffelin]
Generated description
Jacob Schieffelin was an 18th–19th century American merchant and landowner from the prominent Schieffelin family, known for his role in early New York commerce and real estate development.

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_69eefaceb2a08190b9659b7f730629f5 completed April 27, 2026, 5:57 a.m.
NER Named-entity recognition batch_69f6250512dc81909b88128fa88eef9a completed May 2, 2026, 4:23 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12482b7ab08190a2e8c113a55c0310 completed May 24, 2026, 12:36 a.m.
NEDg Description generation batch_6a1249e0bae48190b1ccf396b459793f completed May 24, 2026, 12:44 a.m.
NED2 Entity disambiguation (via description) batch_6a124ab7e38c8190a1b7157d53c3d858 completed May 24, 2026, 12:47 a.m.
Created at: April 27, 2026, 9:16 a.m.