Triple

T9930029
Position Surface form Disambiguated ID Type / Status
Subject Bangka E192623 entity
Predicate hasCity P316 FINISHED
Object Toboali
Toboali is a coastal town and administrative center in the southern part of Bangka Island in Indonesia, known historically for its tin mining activities.
 E829260 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: Toboali | Statement: [Bangka, hasCity, Toboali]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Toboali
Context triple: [Bangka, hasCity, Toboali]
  • A. Belawa
    Belawa is a town and administrative district located within Wajo Regency in South Sulawesi, Indonesia.
  • B. Bambarra
    Bambarra is a small coastal village on Middle Caicos in the Turks and Caicos Islands, known for its historical links to African heritage and its tranquil, undeveloped beaches.
  • C. Boengkoe
    Boengkoe is an alternative name for the Bungku language, an Austronesian language spoken in Southeast Sulawesi, Indonesia.
  • D. Lumban
    Lumban is a municipality in the Philippine province of Laguna known for its traditional hand-embroidered textiles and scenic lakeside setting along Laguna de Bay.
  • E. Bolango-Bulango
    Bolango-Bulango is an Austronesian language spoken by the Bolango people in northern Sulawesi, Indonesia.
  • 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: Toboali
Triple: [Bangka, hasCity, Toboali]
Generated description
Toboali is a coastal town and administrative center in the southern part of Bangka Island in Indonesia, known historically for its tin mining activities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Toboali
Target entity description: Toboali is a coastal town and administrative center in the southern part of Bangka Island in Indonesia, known historically for its tin mining activities.
  • A. Belawa
    Belawa is a town and administrative district located within Wajo Regency in South Sulawesi, Indonesia.
  • B. Bambarra
    Bambarra is a small coastal village on Middle Caicos in the Turks and Caicos Islands, known for its historical links to African heritage and its tranquil, undeveloped beaches.
  • C. Boengkoe
    Boengkoe is an alternative name for the Bungku language, an Austronesian language spoken in Southeast Sulawesi, Indonesia.
  • D. Lumban
    Lumban is a municipality in the Philippine province of Laguna known for its traditional hand-embroidered textiles and scenic lakeside setting along Laguna de Bay.
  • E. Bolango-Bulango
    Bolango-Bulango is an Austronesian language spoken by the Bolango people in northern Sulawesi, Indonesia.
  • 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_69ca82dd978c8190947124ab0d3315ac completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdb5b4196881909a004091a4203c45 completed April 2, 2026, 12:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69d20e258e888190ae4e2abac80e3399 completed April 5, 2026, 7:24 a.m.
NEDg Description generation batch_69d21229a1188190899df0d8d7f78a7d completed April 5, 2026, 7:41 a.m.
NED2 Entity disambiguation (via description) batch_69d212a46ee48190aa629a189dee531d completed April 5, 2026, 7:43 a.m.
Created at: March 30, 2026, 8:43 p.m.