Triple

T3282103
Position Surface form Disambiguated ID Type / Status
Subject Falling Man E68894 entity
Predicate hasCharacter P2308 FINISHED
Object Martin
Martin is a character in Don DeLillo’s novel "Falling Man," which explores the personal and psychological aftermath of the September 11 attacks.
 E346105 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: Martin | Statement: [Falling Man, hasCharacter, Martin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Martin
Context triple: [Falling Man, hasCharacter, Martin]
  • A. Martin
    Martin is the given name of Martin Luther King Jr., the prominent American civil rights leader and Baptist minister who advocated nonviolent resistance to racial segregation.
  • B. Martin
    Martin is a minor but kind-hearted character in Ernest Hemingway's novella "The Old Man and the Sea," known for helping the old fisherman Santiago.
  • C. Martin
    Martin is the central protagonist of the 1991 psychological thriller film "Proof," around whom the story’s exploration of trust, perception, and human connection revolves.
  • D. Martin
    Martin is a pessimistic scholar who serves as one of Candide’s key philosophical foils in Voltaire’s satirical novella "Candide."
  • E. Martin
    Martin was the first name of Martin Luther, a prominent Nazi official who served as a diplomat in the German Foreign Office during the Third Reich.
  • 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: Martin
Triple: [Falling Man, hasCharacter, Martin]
Generated description
Martin is a character in Don DeLillo’s novel "Falling Man," which explores the personal and psychological aftermath of the September 11 attacks.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Martin
Target entity description: Martin is a character in Don DeLillo’s novel "Falling Man," which explores the personal and psychological aftermath of the September 11 attacks.
  • A. Martin
    Martin is the central protagonist of the 1991 psychological thriller film "Proof," around whom the story’s exploration of trust, perception, and human connection revolves.
  • B. Martin
    Martin is a minor but kind-hearted character in Ernest Hemingway's novella "The Old Man and the Sea," known for helping the old fisherman Santiago.
  • C. Martin
    Martin is a pessimistic scholar who serves as one of Candide’s key philosophical foils in Voltaire’s satirical novella "Candide."
  • D. Martin
    Martin is the given name of Martin Luther King Jr., the prominent American civil rights leader and Baptist minister who advocated nonviolent resistance to racial segregation.
  • E. Martin
    Martin was the first name of Martin Luther, a prominent Nazi official who served as a diplomat in the German Foreign Office during the Third Reich.
  • 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_69ad859c463481909ca4be267336c290 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb0345d448190a1f936abe7748e33 completed March 8, 2026, 5:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2f3ca2fdc81908ba4f304c63989ea completed March 12, 2026, 5:11 p.m.
NEDg Description generation batch_69b2f9748a708190bc4af97f3a1fabff completed March 12, 2026, 5:35 p.m.
NED2 Entity disambiguation (via description) batch_69b30a6aa45c8190bb21ae0cd649aab4 completed March 12, 2026, 6:48 p.m.
Created at: March 8, 2026, 3:10 p.m.