Triple

T9109549
Position Surface form Disambiguated ID Type / Status
Subject Arkhangelsk E218560 entity
Predicate namedAfter P63 FINISHED
Object Archangel Michael E50376 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: Archangel Michael | Statement: [Arkhangelsk, namedAfter, Archangel Michael]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Archangel Michael
Context triple: [Arkhangelsk, namedAfter, Archangel Michael]
  • A. Saint Michael chosen
    Saint Michael is an archangel in Christian, Jewish, and Islamic traditions, revered as a protector, leader of the heavenly hosts, and vanquisher of evil.
  • B. Arcángel
    Arcángel is a Puerto Rican-American reggaeton and Latin trap singer and songwriter known for his influential role in the urban Latin music scene.
  • C. Seraphim
    Seraphim are the highest-ranking class of angels in Christian angelology, traditionally associated with fiery devotion and the worship of God.
  • D. Raphael (archangel)
    Raphael is an archangel in Judeo-Christian tradition, often associated with healing and guidance, notably appearing in the deuterocanonical Book of Tobit.
  • E. Angel
    "Angel" is a 1937 romantic comedy film directed by Ernst Lubitsch, known for its sophisticated wit and starring Marlene Dietrich, Herbert Marshall, and Melvyn Douglas.
  • 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_69ca83db7448819090d0a5de842ef2ac completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cca845d9b0819084230e7cdd92dee0 completed April 1, 2026, 5:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69d03038dba48190991cb76576349bc3 completed April 3, 2026, 9:25 p.m.
Created at: March 30, 2026, 7:16 p.m.