Triple

T15174345
Position Surface form Disambiguated ID Type / Status
Subject Barretos E362565 entity
Predicate hasAirport P105 FINISHED
Object Chafei Amsei Airport
Chafei Amsei Airport is a regional public airport serving the city of Barretos in the state of São Paulo, Brazil.
E1156584 NE FINISHED

How this triple was built (4 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: Chafei Amsei Airport | Statement: [Barretos, hasAirport, Chafei Amsei Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chafei Amsei Airport
Context triple: [Barretos, hasAirport, Chafei Amsei Airport]
  • A. Muanda Airport
    Muanda Airport is a public airport serving the coastal town of Muanda in the western Democratic Republic of the Congo, providing regional air connectivity for passengers and cargo.
  • B. Ulei Airport
    Ulei Airport is a small regional airfield serving the island of Ambrym in Vanuatu, providing local and inter-island air connections.
  • C. Sonari Airport
    Sonari Airport is a domestic airport serving the industrial city of Jamshedpur in the Indian state of Jharkhand.
  • D. Rinas Airport
    Rinas Airport is the main international airport serving Tirana and the primary air gateway to Albania.
  • E. Falalop Airport
    Falalop Airport is a small airstrip serving the remote Ulithi Atoll in the Federated States of Micronesia, providing vital air access for residents and visitors.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Chafei Amsei Airport
Triple: [Barretos, hasAirport, Chafei Amsei Airport]
Generated description
Chafei Amsei Airport is a regional public airport serving the city of Barretos in the state of São Paulo, Brazil.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Chafei Amsei Airport
Target entity description: Chafei Amsei Airport is a regional public airport serving the city of Barretos in the state of São Paulo, Brazil.
  • A. Muanda Airport
    Muanda Airport is a public airport serving the coastal town of Muanda in the western Democratic Republic of the Congo, providing regional air connectivity for passengers and cargo.
  • B. Ulei Airport
    Ulei Airport is a small regional airfield serving the island of Ambrym in Vanuatu, providing local and inter-island air connections.
  • C. Sonari Airport
    Sonari Airport is a domestic airport serving the industrial city of Jamshedpur in the Indian state of Jharkhand.
  • D. Rinas Airport
    Rinas Airport is the main international airport serving Tirana and the primary air gateway to Albania.
  • E. Falalop Airport
    Falalop Airport is a small airstrip serving the remote Ulithi Atoll in the Federated States of Micronesia, providing vital air access for residents and visitors.
  • F. None of above. chosen

Provenance (5 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_69d85a087b7c81908baa94a53dac8d68 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e0066236d481909e8ac47f496861ad completed April 15, 2026, 9:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff1a61136081908198806944c81808 completed May 9, 2026, 11:28 a.m.
NEDg Description generation batch_69ff1bc3aa2c8190b61d21fa78682ea1 completed May 9, 2026, 11:34 a.m.
NED2 Entity disambiguation (via description) batch_69ff1cbccab48190b9e19b5a09324d8c completed May 9, 2026, 11:38 a.m.
Created at: April 10, 2026, 3:09 a.m.