Triple

T16640051
Position Surface form Disambiguated ID Type / Status
Subject Birthday Cake E404306 entity
Predicate writer P1360 FINISHED
Object Marcos Palacios E1067413 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: Marcos Palacios | Statement: [Birthday Cake, writer, Marcos Palacios]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marcos Palacios
Context triple: [Birthday Cake, writer, Marcos Palacios]
  • A. Marcos Palacios chosen
    Marcos Palacios is a writer associated with the Anaconda project, likely contributing documentation, code, or related written materials.
  • B. Santiago Aguirre
    Santiago Aguirre is the central protagonist of the Spanish mystery-drama television series "High Seas," around whom much of the show's intrigue and character dynamics revolve.
  • C. Sergio Valenzuela
    Sergio Valenzuela is a notable individual distinguished enough in his field or public life to be specifically recognized as a bearer of the Valenzuela surname.
  • D. Roberto Reyes
    Roberto Reyes is a Marvel Comics character who becomes the supernatural antihero Ghost Rider, known for driving a flaming muscle car instead of the traditional motorcycle.
  • E. Héctor Jiménez
    Héctor Jiménez is a Mexican actor and comedian best known for his quirky supporting roles in films such as "Nacho Libre" and various genre and independent movies.
  • 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_69d8838a41f08190b0c3f79c47df5078 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e37ad0e5408190aef8b5577be73057 completed April 18, 2026, 12:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a015fb40dc08190b9d6a04f3c19f57d completed May 11, 2026, 4:48 a.m.
Created at: April 10, 2026, 5:18 a.m.