Triple

T7194858
Position Surface form Disambiguated ID Type / Status
Subject Fernando Luis Ribas Dominicci Airport E168587 entity
Predicate serves P98 FINISHED
Object San Juan E663 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: San Juan | Statement: [Fernando Luis Ribas Dominicci Airport, serves, San Juan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Juan
Context triple: [Fernando Luis Ribas Dominicci Airport, serves, San Juan]
  • A. San Juan
    San Juan is a coastal municipality on Siquijor Island in the Philippines known for its beaches, dive spots, and laid-back tourist resorts.
  • B. San Juan chosen
    San Juan is the largest city and main cultural, economic, and tourism hub of Puerto Rico, known for its historic colonial architecture and vibrant coastal setting.
  • C. San Juan
    San Juan is an Argentine wine-producing region recognized for its significant Malbec production.
  • D. San Juan
    San Juan is a suburban town in Trinidad and Tobago located just east of the capital, Port of Spain, known for its bustling commercial activity and residential communities.
  • E. San Juan
    San Juan is a highly urbanized city in Metro Manila, Philippines, known for its historical sites, dense residential and commercial areas, and role in the capital region’s urban core.
  • 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_69c68a5376748190bb500f03df86e93e completed March 27, 2026, 1:46 p.m.
NER Named-entity recognition batch_69c6e9050164819081fd6a11d10f9833 completed March 27, 2026, 8:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69c83c4097d88190b00a8c64ce6871e5 completed March 28, 2026, 8:38 p.m.
Created at: March 27, 2026, 2:51 p.m.