Triple

T21305211
Position Surface form Disambiguated ID Type / Status
Subject Kirovabad E525177 entity
Predicate successorName P78 FINISHED
Object Ganja NE NERFINISHED

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: Ganja | Statement: [Kirovabad, successorName, Ganja]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ganja
Context triple: [Kirovabad, successorName, Ganja]
  • A. Ganja chosen
    Ganja is one of Azerbaijan’s largest and oldest cities, known as a historic cultural and economic center in the South Caucasus.
  • B. União do Vegetal
    União do Vegetal is a Brazilian syncretic religious organization that uses the psychoactive tea ayahuasca as a sacrament in its spiritual ceremonies.
  • C. Cannabis
    Cannabis is a genus of flowering plants best known for producing psychoactive and medicinal compounds such as THC and CBD, widely used for recreational, therapeutic, and industrial (hemp) purposes.
  • D. Sahl Hasheesh
    Sahl Hasheesh is a modern Red Sea coastal resort town in Egypt known for its luxury hotels, beaches, and diving and snorkeling sites.
  • E. Sabu
    Sabu is a pioneering hardcore professional wrestler best known for his extreme, high-risk style and influential run in ECW.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b518b8948190ad69cf9a8784d397 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e75aa341908190963c4368708860b8 completed April 21, 2026, 11:08 a.m.
Created at: April 16, 2026, 4:05 p.m.