Triple

T12061639
Position Surface form Disambiguated ID Type / Status
Subject Dan Marino E287184 entity
Predicate child P120 FINISHED
Object Daniel Marino III E287184 NE FINISHED

How this triple was built (2 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: Daniel Marino III | Statement: [Dan Marino, child, Daniel Marino III]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Daniel Marino III
Context triple: [Dan Marino, child, Daniel Marino III]
  • A. Daniel Marino III chosen
    Daniel Marino III is the son of Pro Football Hall of Fame quarterback Dan Marino.
  • B. Jamal Manning
    Jamal Manning is a fictional Chicago crime boss and aspiring politician who serves as one of the primary antagonists in the heist thriller film "Widows" (2018).
  • C. DeSean Jackson
    DeSean Jackson is an American former NFL wide receiver and return specialist known for his explosive speed and game-changing big plays, including iconic punt return touchdowns.
  • D. Tre'Von Willis
    Tre'Von Willis is an American former college basketball guard best known for his standout career at UNLV, where he emerged as one of the Mountain West Conference’s top players.
  • E. Vinny Testaverde
    Vinny Testaverde is a former American football quarterback who starred at the University of Miami before enjoying a long NFL career with multiple teams.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d6ab4846e081908ee7bbd66a6d3459 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d9043f82248190b05692aa0dc178a8 completed April 10, 2026, 2:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f6532a048190b53f96c9df948dda completed May 2, 2026, 1:04 p.m.
Created at: April 8, 2026, 9:48 p.m.