Triple

T17104264
Position Surface form Disambiguated ID Type / Status
Subject Minangkabau International Airport E415056 entity
Predicate locatedNear P294 FINISHED
Object Ketaping
Ketaping is a locality in West Sumatra, Indonesia, known primarily for being the area served by Minangkabau International Airport near Padang.
E1250475 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: Ketaping | Statement: [Minangkabau International Airport, locatedNear, Ketaping]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ketaping
Context triple: [Minangkabau International Airport, locatedNear, Ketaping]
  • A. Kabu Kabu
    Kabu Kabu is a speculative fiction short story collection by Nnedi Okorafor that blends African folklore, magical realism, and contemporary themes.
  • B. Sekadau
    Sekadau is a town and regency capital in the Indonesian province of West Kalimantan on the island of Borneo.
  • C. Kuto-Kute
    Kuto-Kute is a regional dialect of the Sasak language spoken by Sasak communities on the island of Lombok in Indonesia.
  • D. Ngantang
    Ngantang is a district in Malang Regency, East Java, Indonesia, known for its cool highland climate and proximity to the Selorejo Dam and surrounding agricultural landscapes.
  • E. Bupkis
    Bupkis is a semi-autobiographical comedy series created by and starring Pete Davidson that blends surreal humor with dramatized versions of his real life.
  • 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: Ketaping
Triple: [Minangkabau International Airport, locatedNear, Ketaping]
Generated description
Ketaping is a locality in West Sumatra, Indonesia, known primarily for being the area served by Minangkabau International Airport near Padang.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Ketaping
Target entity description: Ketaping is a locality in West Sumatra, Indonesia, known primarily for being the area served by Minangkabau International Airport near Padang.
  • A. Kabu Kabu
    Kabu Kabu is a speculative fiction short story collection by Nnedi Okorafor that blends African folklore, magical realism, and contemporary themes.
  • B. Sekadau
    Sekadau is a town and regency capital in the Indonesian province of West Kalimantan on the island of Borneo.
  • C. Kuto-Kute
    Kuto-Kute is a regional dialect of the Sasak language spoken by Sasak communities on the island of Lombok in Indonesia.
  • D. Ngantang
    Ngantang is a district in Malang Regency, East Java, Indonesia, known for its cool highland climate and proximity to the Selorejo Dam and surrounding agricultural landscapes.
  • E. Bupkis
    Bupkis is a semi-autobiographical comedy series created by and starring Pete Davidson that blends surreal humor with dramatized versions of his real life.
  • 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_69d886cfc8e88190b05ba466edd35591 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3dc2591a881909c5f4f7db47f4d6c completed April 18, 2026, 7:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0139ffbe808190a24e827331ee4a6c completed May 11, 2026, 2:07 a.m.
NEDg Description generation batch_6a013ae388548190b09d2c81e1ab0d02 completed May 11, 2026, 2:11 a.m.
NED2 Entity disambiguation (via description) batch_6a013b4df74c81908b3b99e276531e13 completed May 11, 2026, 2:13 a.m.
Created at: April 10, 2026, 5:35 a.m.