Triple

T3250469
Position Surface form Disambiguated ID Type / Status
Subject Weiss E68163 entity
Predicate hasNotableBearer P458 FINISHED
Object Robert Weiss
Robert Weiss is a relatively common personal name shared by multiple individuals across various professions, including the arts, sciences, and public life.
 E375295 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: Robert Weiss | Statement: [Weiss, hasNotableBearer, Robert Weiss]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Robert Weiss
Context triple: [Weiss, hasNotableBearer, Robert Weiss]
  • A. Andrew Weiss
    Andrew Weiss is a notable individual whose specific prominence or field of recognition is not clearly identifiable from the given information alone.
  • B. Andrew Weisblum
    Andrew Weisblum is an American film editor known for his work on major feature films, including collaborations with directors like Darren Aronofsky and Wes Anderson.
  • C. John Weiss
    John Weiss is a relatively obscure individual whose specific notability is not clearly established from the given information.
  • D. Michael Weiss
    Michael Weiss is a common personal name shared by multiple notable individuals across fields such as journalism, sports, and the arts.
  • E. David Weiss
    David Weiss is a common personal name shared by multiple notable individuals across fields such as law, music, and literature.
  • 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: Robert Weiss
Triple: [Weiss, hasNotableBearer, Robert Weiss]
Generated description
Robert Weiss is a relatively common personal name shared by multiple individuals across various professions, including the arts, sciences, and public life.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Robert Weiss
Target entity description: Robert Weiss is a relatively common personal name shared by multiple individuals across various professions, including the arts, sciences, and public life.
  • A. Andrew Weiss
    Andrew Weiss is a notable individual whose specific prominence or field of recognition is not clearly identifiable from the given information alone.
  • B. Andrew Weisblum
    Andrew Weisblum is an American film editor known for his work on major feature films, including collaborations with directors like Darren Aronofsky and Wes Anderson.
  • C. John Weiss
    John Weiss is a relatively obscure individual whose specific notability is not clearly established from the given information.
  • D. Michael Weiss
    Michael Weiss is a common personal name shared by multiple notable individuals across fields such as journalism, sports, and the arts.
  • E. David Weiss
    David Weiss is a common personal name shared by multiple notable individuals across fields such as law, music, and literature.
  • 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_69ad858e4c708190aa31d486cfee8a6a completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaf40f7908190a450c3136fccb020 completed March 8, 2026, 5:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69b44ed5f23c8190bfd1fb6370aad390 completed March 13, 2026, 5:52 p.m.
NEDg Description generation batch_69b451c988f081909adfe88a9e027eb9 completed March 13, 2026, 6:04 p.m.
NED2 Entity disambiguation (via description) batch_69b45a7d1bcc8190b2c69a0fffa94cd5 completed March 13, 2026, 6:42 p.m.
Created at: March 8, 2026, 3:09 p.m.