Triple

T12548448
Position Surface form Disambiguated ID Type / Status
Subject Chita E300031 entity
Predicate hasAirport P105 FINISHED
Object Kadala Airport
Kadala Airport is the main commercial airport serving the city of Chita in eastern Siberia, Russia.
E1017288 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: Kadala Airport | Statement: [Chita, hasAirport, Kadala Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kadala Airport
Context triple: [Chita, hasAirport, Kadala Airport]
  • A. 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.
  • B. Kalemie Airport
    Kalemie Airport is a public airport serving the town of Kalemie in the Tanganyika Province of the Democratic Republic of the Congo, providing regional air transport connections.
  • C. Dumna Airport
    Dumna Airport is a domestic airport serving the city of Jabalpur in the Indian state of Madhya Pradesh.
  • D. 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.
  • E. Dumatubin Airport
    Dumatubin Airport is a small regional airport serving the Kai Islands in Indonesia, providing domestic air connections to this remote archipelago.
  • 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: Kadala Airport
Triple: [Chita, hasAirport, Kadala Airport]
Generated description
Kadala Airport is the main commercial airport serving the city of Chita in eastern Siberia, Russia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Kadala Airport
Target entity description: Kadala Airport is the main commercial airport serving the city of Chita in eastern Siberia, Russia.
  • A. 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.
  • B. Kalemie Airport
    Kalemie Airport is a public airport serving the town of Kalemie in the Tanganyika Province of the Democratic Republic of the Congo, providing regional air transport connections.
  • C. Dumna Airport
    Dumna Airport is a domestic airport serving the city of Jabalpur in the Indian state of Madhya Pradesh.
  • D. 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.
  • E. Dumatubin Airport
    Dumatubin Airport is a small regional airport serving the Kai Islands in Indonesia, providing domestic air connections to this remote archipelago.
  • 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_69d6ada707008190aaec1238117c9379 completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d95481ba28819099f7cd2de02e8837 completed April 10, 2026, 7:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6cbb096d881908dfd2a7126632d96 completed May 3, 2026, 4:14 a.m.
NEDg Description generation batch_69f6cd3d5090819091b65f544ad139fd completed May 3, 2026, 4:21 a.m.
NED2 Entity disambiguation (via description) batch_69f6cdc8d52c819083717a455d589646 completed May 3, 2026, 4:23 a.m.
Created at: April 8, 2026, 9:58 p.m.