Triple

T22092350
Position Surface form Disambiguated ID Type / Status
Subject Parker E545941 entity
Predicate character P662 FINISHED
Object Melander
Melander is a fictional character appearing in works featuring the character Parker, the professional thief created by novelist Donald E. Westlake under the pseudonym Richard Stark.
 E1519175 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: Melander | Statement: [Parker, character, Melander]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Melander
Context triple: [Parker, character, Melander]
  • A. Selander
    Selander is a surname most notably associated with American film director Lesley Selander, known for his prolific work in Westerns.
  • B. Relander
    Relander is a Finnish surname most notably associated with Lauri Kristian Relander, the second President of Finland.
  • C. Gyllensten
    Gyllensten is a Swedish surname most notably associated with Lars Gyllensten, a prominent author and former member of the Swedish Academy.
  • D. Söderblom
    Söderblom is a Swedish surname most notably associated with Nathan Söderblom, the Nobel Peace Prize–winning Lutheran archbishop and ecumenical leader.
  • E. Linderud
    Linderud is a residential neighborhood in Oslo, Norway, known for its apartment blocks, shopping center, and access to public transportation.
  • 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: Melander
Triple: [Parker, character, Melander]
Generated description
Melander is a fictional character appearing in works featuring the character Parker, the professional thief created by novelist Donald E. Westlake under the pseudonym Richard Stark.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Melander
Target entity description: Melander is a fictional character appearing in works featuring the character Parker, the professional thief created by novelist Donald E. Westlake under the pseudonym Richard Stark.
  • A. Selander
    Selander is a surname most notably associated with American film director Lesley Selander, known for his prolific work in Westerns.
  • B. Relander
    Relander is a Finnish surname most notably associated with Lauri Kristian Relander, the second President of Finland.
  • C. Gyllensten
    Gyllensten is a Swedish surname most notably associated with Lars Gyllensten, a prominent author and former member of the Swedish Academy.
  • D. Söderblom
    Söderblom is a Swedish surname most notably associated with Nathan Söderblom, the Nobel Peace Prize–winning Lutheran archbishop and ecumenical leader.
  • E. Linderud
    Linderud is a residential neighborhood in Oslo, Norway, known for its apartment blocks, shopping center, and access to public transportation.
  • 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_69e11e36d03c8190a83a1ba802b7231b completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f128e6b1d881909bf0f4a52199354c completed April 28, 2026, 9:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a879b9130819089f8e2b7106f875b completed May 18, 2026, 3:29 a.m.
NEDg Description generation batch_6a0a891eb0708190a4575a01f45b98aa completed May 18, 2026, 3:35 a.m.
NED2 Entity disambiguation (via description) batch_6a0a89977c8c8190a5c87d1c2b68ed48 completed May 18, 2026, 3:37 a.m.
Created at: April 16, 2026, 8:29 p.m.