Triple

T7467787
Position Surface form Disambiguated ID Type / Status
Subject Rigu E176418 entity
Predicate wornBy P271 FINISHED
Object Dimasa women E237119 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: Dimasa women | Statement: [Rigu, wornBy, Dimasa women]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dimasa women
Context triple: [Rigu, wornBy, Dimasa women]
  • A. Dimasa
    Dimasa is a Tibeto-Burman language spoken primarily by the Dimasa people in parts of Northeast India, including the Barak Valley region.
  • B. Dimasa community chosen
    The Dimasa community is an indigenous ethnic group of Northeast India, known for its distinct Tibeto-Burman language, rich folk traditions, and historical kingdom in the region now spanning parts of Assam and Nagaland.
  • C. Ladies
    The Ladies are the women's athletic teams representing Centenary College of Louisiana in intercollegiate sports.
  • D. The Women’s
    The Women’s is a major specialist public hospital in Melbourne, Australia, dedicated to women’s health, maternity, and newborn care.
  • E. Women
    "Women" is a semi-autobiographical novel by Charles Bukowski that follows his hard-drinking alter ego Henry Chinaski through a series of raw, often chaotic relationships with various women.
  • 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_69c69f223fd88190b4c69b95d7cbeeda completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f3f589cc81909f25268838c7c964 completed March 27, 2026, 9:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69c83475392c8190a51d24e1530c0c83 completed March 28, 2026, 8:05 p.m.
Created at: March 27, 2026, 3:40 p.m.