Triple

T1565827
Position Surface form Disambiguated ID Type / Status
Subject Tabora Region E33430 entity
Predicate capital P234 FINISHED
Object Tabora
Tabora is a historic town in western Tanzania known as a regional trade center and former hub of 19th-century caravan routes.
 E180266 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: Tabora | Statement: [Tabora Region, capital, Tabora]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tabora
Context triple: [Tabora Region, capital, Tabora]
  • A. Kumba
    Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
  • B. Mpanda
    Mpanda is a town in western Tanzania that serves as an important administrative and commercial hub for the surrounding region.
  • C. Matadi
    Matadi is a major port city in western Democratic Republic of the Congo, serving as the country’s principal seaport and a key gateway for trade between the Atlantic Ocean and the interior via the Congo River.
  • D. Tabora Region
    Tabora Region is an inland administrative region in western Tanzania known historically as a key hub for trade and rail transport.
  • E. Negombo
    Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
  • 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: Tabora
Triple: [Tabora Region, capital, Tabora]
Generated description
Tabora is a historic town in western Tanzania known as a regional trade center and former hub of 19th-century caravan routes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tabora
Target entity description: Tabora is a historic town in western Tanzania known as a regional trade center and former hub of 19th-century caravan routes.
  • A. Kumba
    Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
  • B. Mpanda
    Mpanda is a town in western Tanzania that serves as an important administrative and commercial hub for the surrounding region.
  • C. Matadi
    Matadi is a major port city in western Democratic Republic of the Congo, serving as the country’s principal seaport and a key gateway for trade between the Atlantic Ocean and the interior via the Congo River.
  • D. Tabora Region
    Tabora Region is an inland administrative region in western Tanzania known historically as a key hub for trade and rail transport.
  • E. Negombo
    Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
  • 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_69a885f11b048190935025a035302715 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69abb2308bec81909d1660934eff171b completed March 7, 2026, 5:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad469474c88190b80d6d7a30c9e19d completed March 8, 2026, 9:51 a.m.
NEDg Description generation batch_69ad470069f08190b886041d1a1c7707 completed March 8, 2026, 9:53 a.m.
NED2 Entity disambiguation (via description) batch_69ad475d9528819086546aae6db74e19 completed March 8, 2026, 9:54 a.m.
Created at: March 4, 2026, 7:27 p.m.