Triple

T14980313
Position Surface form Disambiguated ID Type / Status
Subject Lahij Governorate E373556 entity
Predicate hasMajorCity P316 FINISHED
Object Tuban
Tuban is a major city in Yemen’s Lahij Governorate, serving as an important local center for administration and commerce.
 E1138014 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: Tuban | Statement: [Lahij Governorate, hasMajorCity, Tuban]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tuban
Context triple: [Lahij Governorate, hasMajorCity, Tuban]
  • A. Tuban
    Tuban is a coastal town and regency capital in northern East Java, Indonesia, known historically as a trading port and for its cultural and religious heritage sites.
  • B. Nganjuk
    Nganjuk is a regency capital and regional urban center in the province of East Java, Indonesia.
  • C. Tulungagung
    Tulungagung is a regency and urban center in southern East Java, Indonesia, known for its marble industry and coastal landscapes along the Indian Ocean.
  • D. Citeureup
    Citeureup is a district in West Java, Indonesia, known as one of the industrial and residential areas within the Bogor metropolitan region.
  • E. Blitar
    Blitar is a city in East Java, Indonesia, best known as the hometown and final resting place of the country’s first president, Sukarno.
  • 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: Tuban
Triple: [Lahij Governorate, hasMajorCity, Tuban]
Generated description
Tuban is a major city in Yemen’s Lahij Governorate, serving as an important local center for administration and commerce.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tuban
Target entity description: Tuban is a major city in Yemen’s Lahij Governorate, serving as an important local center for administration and commerce.
  • A. Tuban
    Tuban is a coastal town and regency capital in northern East Java, Indonesia, known historically as a trading port and for its cultural and religious heritage sites.
  • B. Nganjuk
    Nganjuk is a regency capital and regional urban center in the province of East Java, Indonesia.
  • C. Tulungagung
    Tulungagung is a regency and urban center in southern East Java, Indonesia, known for its marble industry and coastal landscapes along the Indian Ocean.
  • D. Citeureup
    Citeureup is a district in West Java, Indonesia, known as one of the industrial and residential areas within the Bogor metropolitan region.
  • E. Blitar
    Blitar is a city in East Java, Indonesia, best known as the hometown and final resting place of the country’s first president, Sukarno.
  • 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_69d85ccbbcd48190acb56e7cf104d8ad completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded6fcebf481909f72cab577560d82 completed April 15, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69feb7d709fc8190990a72faf0af5ee3 completed May 9, 2026, 4:28 a.m.
NEDg Description generation batch_69feb8f53b608190b92cc09b09340f98 completed May 9, 2026, 4:32 a.m.
NED2 Entity disambiguation (via description) batch_69feb9241e988190871393d6e06e88e8 completed May 9, 2026, 4:33 a.m.
Created at: April 10, 2026, 2:52 a.m.