Triple

T1366175
Position Surface form Disambiguated ID Type / Status
Subject East Java E30008 entity
Predicate hasMajorCity P316 FINISHED
Object Gresik
Gresik is an industrial and port city in Indonesia known for its cement production and role as part of the Surabaya metropolitan area.
E199272 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: Gresik | Statement: [East Java, hasMajorCity, Gresik]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gresik
Context triple: [East Java, hasMajorCity, Gresik]
  • A. Pasuruan
    Pasuruan is a city in East Java, Indonesia, known as a gateway to the popular Mount Bromo volcanic tourism area.
  • B. Mojokerto
    Mojokerto is a city in Indonesia known for its historical significance as part of the former Majapahit Empire and its location in the province of East Java.
  • C. Probolinggo
    Probolinggo is a coastal city in East Java, Indonesia, known as a common gateway for tourists visiting the Mount Bromo volcanic area.
  • D. Kediri
    Kediri is a historic city in Indonesia known for its role as a former Javanese kingdom center and as an important economic hub in modern East Java.
  • E. Jember
    Jember is a regency and major urban center in eastern Java, Indonesia, known for its agricultural economy and cultural festivals such as the Jember Fashion Carnaval.
  • 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: Gresik
Triple: [East Java, hasMajorCity, Gresik]
Generated description
Gresik is an industrial and port city in Indonesia known for its cement production and role as part of the Surabaya metropolitan area.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Gresik
Target entity description: Gresik is an industrial and port city in Indonesia known for its cement production and role as part of the Surabaya metropolitan area.
  • A. Pasuruan
    Pasuruan is a city in East Java, Indonesia, known as a gateway to the popular Mount Bromo volcanic tourism area.
  • B. Mojokerto
    Mojokerto is a city in Indonesia known for its historical significance as part of the former Majapahit Empire and its location in the province of East Java.
  • C. Probolinggo
    Probolinggo is a coastal city in East Java, Indonesia, known as a common gateway for tourists visiting the Mount Bromo volcanic area.
  • D. Kediri
    Kediri is a historic city in Indonesia known for its role as a former Javanese kingdom center and as an important economic hub in modern East Java.
  • E. Jember
    Jember is a regency and major urban center in eastern Java, Indonesia, known for its agricultural economy and cultural festivals such as the Jember Fashion Carnaval.
  • 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_69a498f912008190a376a98b207b2071 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c2d1d15481909d58b6fd8aa2e585 completed March 1, 2026, 10:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69ada95d71888190aa49a3011ea2a1bc completed March 8, 2026, 4:52 p.m.
NEDg Description generation batch_69adae972d1081909cd13e8220c3ccc6 completed March 8, 2026, 5:15 p.m.
NED2 Entity disambiguation (via description) batch_69adaf9d042481909dbd54d9e04e444e completed March 8, 2026, 5:19 p.m.
Created at: March 1, 2026, 7:57 p.m.