Triple

T1385213
Position Surface form Disambiguated ID Type / Status
Subject West Java E29829 entity
Predicate hasMajorCity P316 FINISHED
Object Depok
Depok is a rapidly growing commuter city in Indonesia located between Jakarta and Bogor, known for its universities and residential developments.
 E175029 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: Depok | Statement: [West Java, hasMajorCity, Depok]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Depok
Context triple: [West Java, hasMajorCity, Depok]
  • A. Bekasi
    Bekasi is a large, rapidly growing industrial and residential city in the Greater Jakarta metropolitan area of Indonesia.
  • B. Bogor
    Bogor is a city on the Indonesian island of Java known for its cool climate, botanical gardens, and role as a major educational and research center.
  • C. Bogor Regency
    Bogor Regency is an administrative region in West Java, Indonesia, that encircles the city of Bogor and is known for its rapidly growing suburban and rural communities.
  • D. Tasikmalaya
    Tasikmalaya is a significant city in West Java, Indonesia, known as an important cultural and economic hub for the Sundanese people.
  • E. Serang
    Serang is the capital city of Banten Province on the western tip of Java, Indonesia, serving as an important regional administrative and economic center.
  • 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: Depok
Triple: [West Java, hasMajorCity, Depok]
Generated description
Depok is a rapidly growing commuter city in Indonesia located between Jakarta and Bogor, known for its universities and residential developments.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Depok
Target entity description: Depok is a rapidly growing commuter city in Indonesia located between Jakarta and Bogor, known for its universities and residential developments.
  • A. Bekasi
    Bekasi is a large, rapidly growing industrial and residential city in the Greater Jakarta metropolitan area of Indonesia.
  • B. Bogor
    Bogor is a city on the Indonesian island of Java known for its cool climate, botanical gardens, and role as a major educational and research center.
  • C. Bogor Regency
    Bogor Regency is an administrative region in West Java, Indonesia, that encircles the city of Bogor and is known for its rapidly growing suburban and rural communities.
  • D. Tasikmalaya
    Tasikmalaya is a significant city in West Java, Indonesia, known as an important cultural and economic hub for the Sundanese people.
  • E. Serang
    Serang is the capital city of Banten Province on the western tip of Java, Indonesia, serving as an important regional administrative and economic center.
  • 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_69a498dc92f8819094a1108f8ac90f43 completed March 1, 2026, 7:51 p.m.
NER Named-entity recognition batch_69a4c33896548190b44f70c9aaaed9b6 completed March 1, 2026, 10:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad293ab8508190ac321c898cd3df39 completed March 8, 2026, 7:46 a.m.
NEDg Description generation batch_69ad2d23e14881909fc5a4d89c0b74f4 completed March 8, 2026, 8:02 a.m.
NED2 Entity disambiguation (via description) batch_69ad2ded1d548190b0c51c460a751017 completed March 8, 2026, 8:06 a.m.
Created at: March 1, 2026, 7:59 p.m.