Triple

T891701
Position Surface form Disambiguated ID Type / Status
Subject Leo Pinsker E19252 entity
Predicate familyName P18 FINISHED
Object Pinsker
Pinsker is a Jewish surname most notably associated with Leo Pinsker, a 19th-century physician and early Zionist activist.
 E105925 NE FINISHED

How this triple was built (4 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: Pinsker | Statement: [Leo Pinsker, familyName, Pinsker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Pinsker
Context triple: [Leo Pinsker, familyName, Pinsker]
  • A. Peters
    Peters is a set of early United States Supreme Court case reports compiled by Richard Peters, later incorporated into the official United States Reports.
  • B. Gassel
    Gassel is a village in the Dutch province of North Brabant, known historically as a separate municipality before being incorporated into a larger administrative unit.
  • C. Peto
    Peto is a minor companion of Sir John Falstaff and Prince Hal in Shakespeare’s Henry IV plays, often depicted as a comic, roguish follower involved in their tavern escapades.
  • D. Parker
    Parker is a common English surname borne by numerous notable individuals across fields such as politics, sports, arts, and science.
  • E. Kamensky
    Kamensky is a Russian surname most notably associated with former professional ice hockey player Valeri Kamensky.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Pinsker
Triple: [Leo Pinsker, familyName, Pinsker]
Generated description
Pinsker is a Jewish surname most notably associated with Leo Pinsker, a 19th-century physician and early Zionist activist.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Pinsker
Target entity description: Pinsker is a Jewish surname most notably associated with Leo Pinsker, a 19th-century physician and early Zionist activist.
  • A. Peters
    Peters is a set of early United States Supreme Court case reports compiled by Richard Peters, later incorporated into the official United States Reports.
  • B. Gassel
    Gassel is a village in the Dutch province of North Brabant, known historically as a separate municipality before being incorporated into a larger administrative unit.
  • C. Peto
    Peto is a minor companion of Sir John Falstaff and Prince Hal in Shakespeare’s Henry IV plays, often depicted as a comic, roguish follower involved in their tavern escapades.
  • D. Parker
    Parker is a common English surname borne by numerous notable individuals across fields such as politics, sports, arts, and science.
  • E. Kamensky
    Kamensky is a Russian surname most notably associated with former professional ice hockey player Valeri Kamensky.
  • F. None of above. chosen

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_69a4939d37188190848be3d426ebc9ae completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4ad019e448190ab991e85dc6d7708 completed March 1, 2026, 9:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7c025464081908032939637248635 completed March 4, 2026, 5:16 a.m.
NEDg Description generation batch_69a7c227893c8190a4ce35637365014f completed March 4, 2026, 5:24 a.m.
NED2 Entity disambiguation (via description) batch_69a7c2f1d0508190ad47eeb8099fd9f9 completed March 4, 2026, 5:28 a.m.
Created at: March 1, 2026, 7:39 p.m.