Triple

T11038591
Position Surface form Disambiguated ID Type / Status
Subject Davao del Norte E260948 entity
Predicate hasCity P316 FINISHED
Object Panabo E240756 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: Panabo | Statement: [Davao del Norte, hasCity, Panabo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Panabo
Context triple: [Davao del Norte, hasCity, Panabo]
  • A. Panabo chosen
    Panabo is a coastal component city in Davao del Norte, Philippines, known for its extensive banana plantations and role in the region’s agricultural economy.
  • B. The Lake City
    The Lake City is the nickname of Acworth, a Georgia city known for its scenic lakes and waterfront recreation.
  • C. Kalona
    Kalona is a small city in southeastern Iowa known for its strong Amish and Mennonite communities and traditional rural culture.
  • D. Wasilla
    Wasilla is a small city in south-central Alaska known as part of the Anchorage metropolitan area and for being the hometown of former governor Sarah Palin.
  • E. Lake City
    Lake City is a nickname for Lake Charles, a city in southwestern Louisiana known for its petrochemical industry, casinos, and proximity to the Gulf Coast.
  • 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_69d6aa979bdc8190bf0e79104cc098c1 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d797fe93b081909d58bfd4b42715f0 completed April 9, 2026, 12:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3a9c669608190af97c461beaf9f31 completed April 18, 2026, 3:56 p.m.
Created at: April 8, 2026, 9:26 p.m.