Triple

T15770740
Position Surface form Disambiguated ID Type / Status
Subject Gorontalo City E382350 entity
Predicate hasAirport P105 FINISHED
Object Jalaluddin Airport
Jalaluddin Airport is a public airport serving Gorontalo and the surrounding region on the island of Sulawesi in Indonesia.
E1175269 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: Jalaluddin Airport | Statement: [Gorontalo City, hasAirport, Jalaluddin Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jalaluddin Airport
Context triple: [Gorontalo City, hasAirport, Jalaluddin Airport]
  • A. Sultan Mahmud Airport
    Sultan Mahmud Airport is the main public airport serving the Malaysian state of Terengganu, handling domestic flights and limited international services.
  • B. Shah Makhdum Airport
    Shah Makhdum Airport is a regional domestic airport serving the city of Rajshahi in western Bangladesh.
  • C. Dalbandin Airport
    Dalbandin Airport is a small domestic airport serving the town of Dalbandin in Balochistan, Pakistan, providing regional air connectivity.
  • D. Baljek Airport
    Baljek Airport is a small regional airport serving the town of Tura and the surrounding Garo Hills region in the Indian state of Meghalaya.
  • E. Humera Airport
    Humera Airport is a regional airport in northwestern Ethiopia that serves the town of Humera and its surrounding area.
  • 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: Jalaluddin Airport
Triple: [Gorontalo City, hasAirport, Jalaluddin Airport]
Generated description
Jalaluddin Airport is a public airport serving Gorontalo and the surrounding region on the island of Sulawesi in Indonesia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Jalaluddin Airport
Target entity description: Jalaluddin Airport is a public airport serving Gorontalo and the surrounding region on the island of Sulawesi in Indonesia.
  • A. Sultan Mahmud Airport
    Sultan Mahmud Airport is the main public airport serving the Malaysian state of Terengganu, handling domestic flights and limited international services.
  • B. Shah Makhdum Airport
    Shah Makhdum Airport is a regional domestic airport serving the city of Rajshahi in western Bangladesh.
  • C. Dalbandin Airport
    Dalbandin Airport is a small domestic airport serving the town of Dalbandin in Balochistan, Pakistan, providing regional air connectivity.
  • D. Baljek Airport
    Baljek Airport is a small regional airport serving the town of Tura and the surrounding Garo Hills region in the Indian state of Meghalaya.
  • E. Humera Airport
    Humera Airport is a regional airport in northwestern Ethiopia that serves the town of Humera and its surrounding area.
  • 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_69d86da09a10819082fe9797b23e4664 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e051962fe08190a6201dd48196a9ee completed April 16, 2026, 3:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff877c5ae88190aeb500bb5f0d73f7 completed May 9, 2026, 7:14 p.m.
NEDg Description generation batch_69ff885d33708190adb157afa7dc2e07 completed May 9, 2026, 7:17 p.m.
NED2 Entity disambiguation (via description) batch_69ff8948cc68819085c3953226236394 completed May 9, 2026, 7:21 p.m.
Created at: April 10, 2026, 4:47 a.m.