Triple

T9748419
Position Surface form Disambiguated ID Type / Status
Subject Cebu region E236374 entity
Predicate contains P35 FINISHED
Object Toledo City E261525 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: Toledo City | Statement: [Cebu region, contains, Toledo City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Toledo City
Context triple: [Cebu region, contains, Toledo City]
  • A. Toledo City chosen
    Toledo City is a coastal component city on the western side of Cebu Island in the Philippines, known for its mining industry and port facilities.
  • B. Toledo
    Toledo is a historic Spanish city renowned for its medieval architecture, cultural heritage, and role as a major political and religious center in Spain’s history.
  • C. Toledo
    Toledo is a major city in northwestern Ohio, known as an industrial and transportation hub on the western end of Lake Erie.
  • D. Toledo, Indiana
    Toledo, Indiana is a small unincorporated rural community located within Huntington County in the U.S. state of Indiana.
  • E. Havana, Ohio
    Havana, Ohio is a small unincorporated community located in Huron County in north-central Ohio.
  • 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_69ca84d4eddc8190996fec1417d2bae8 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9f68f8b88190b44babf5ae17dfef completed April 1, 2026, 10:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1b00d76488190af68cba694dc329c completed April 5, 2026, 12:42 a.m.
Created at: March 30, 2026, 8:23 p.m.