Triple

T22829344
Position Surface form Disambiguated ID Type / Status
Subject Philip M. Klutznick E565754 entity
Predicate familyName P18 FINISHED
Object Klutznick
Klutznick is the surname of Philip M. Klutznick, an American businessman, Jewish community leader, and former U.S. Secretary of Commerce.
 E1557866 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: Klutznick | Statement: [Philip M. Klutznick, familyName, Klutznick]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Klutznick
Context triple: [Philip M. Klutznick, familyName, Klutznick]
  • A. Eppler
    Eppler is a surname of German origin borne by various individuals and families.
  • B. Rostenkowski-Wolowitz
    Rostenkowski-Wolowitz is the hyphenated married surname of Bernadette, a microbiologist character from the TV sitcom "The Big Bang Theory," combining her maiden name Rostenkowski with her husband Howard Wolowitz's surname.
  • C. Rifkind
    Rifkind is a Scottish surname most notably associated with Sir Malcolm Rifkind, a prominent British Conservative politician and former Foreign Secretary.
  • D. Lubbers
    Lubbers is a Dutch surname most notably associated with Ruud Lubbers, the long-serving former Prime Minister of the Netherlands.
  • E. Harlan Hill
    Harlan Hill was a standout NFL wide receiver of the 1950s, best known for his prolific pass-catching and scoring ability with the Chicago Bears.
  • 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: Klutznick
Triple: [Philip M. Klutznick, familyName, Klutznick]
Generated description
Klutznick is the surname of Philip M. Klutznick, an American businessman, Jewish community leader, and former U.S. Secretary of Commerce.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Klutznick
Target entity description: Klutznick is the surname of Philip M. Klutznick, an American businessman, Jewish community leader, and former U.S. Secretary of Commerce.
  • A. Eppler
    Eppler is a surname of German origin borne by various individuals and families.
  • B. Rostenkowski-Wolowitz
    Rostenkowski-Wolowitz is the hyphenated married surname of Bernadette, a microbiologist character from the TV sitcom "The Big Bang Theory," combining her maiden name Rostenkowski with her husband Howard Wolowitz's surname.
  • C. Rifkind
    Rifkind is a Scottish surname most notably associated with Sir Malcolm Rifkind, a prominent British Conservative politician and former Foreign Secretary.
  • D. Lubbers
    Lubbers is a Dutch surname most notably associated with Ruud Lubbers, the long-serving former Prime Minister of the Netherlands.
  • E. Harlan Hill
    Harlan Hill was a standout NFL wide receiver of the 1950s, best known for his prolific pass-catching and scoring ability with the Chicago Bears.
  • 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_69e24585ab1c81909b2b5065d15805d5 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f17e2a0e308190941064965346f890 completed April 29, 2026, 3:42 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0ba7ac71a881908b7a74a0e2ef99f8 completed May 18, 2026, 11:58 p.m.
NEDg Description generation batch_6a0baa5bc3f08190b39b53b20884fab8 completed May 19, 2026, 12:10 a.m.
NED2 Entity disambiguation (via description) batch_6a0babb6135481909132713e41bc6939 completed May 19, 2026, 12:15 a.m.
Created at: April 17, 2026, 3:34 p.m.