Triple

T6698972
Position Surface form Disambiguated ID Type / Status
Subject Province of Mayabeque E152826 entity
Predicate hasMunicipality P847 FINISHED
Object Batabanó E28770 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: Batabanó | Statement: [Province of Mayabeque, hasMunicipality, Batabanó]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Batabanó
Context triple: [Province of Mayabeque, hasMunicipality, Batabanó]
  • A. Batabanó chosen
    Batabanó is a coastal municipality in western Cuba known for its fishing industry and ferry connections to nearby islands.
  • B. Batian
    Batian is the highest peak of Mount Kenya, a prominent volcanic mountain in central Kenya.
  • C. Batbayan
    Batbayan was a 7th-century Bulgar leader, known as the eldest son and successor of Khan Kubrat who briefly ruled the fragmented confederation of Old Great Bulgaria before its dissolution under Khazar pressure.
  • D. Malabanias
    Malabanias is a barangay (village-level administrative district) within Angeles City in Pampanga, Philippines, known for its mixed residential, commercial, and entertainment areas.
  • E. Pilcaniyeu
    Pilcaniyeu is a small town in Argentina’s Patagonia region, located in the Andean area of Río Negro Province and known for its rural character and nearby natural landscapes.
  • 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_69c68807adbc8190b8632df42b39eda0 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d0a6082c819097a4301538399f59 completed March 27, 2026, 6:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7007f2a648190a10792840536035b completed March 27, 2026, 10:11 p.m.
Created at: March 27, 2026, 2:05 p.m.