Triple

T1868284
Position Surface form Disambiguated ID Type / Status
Subject Río Muni E34972 entity
Predicate containsCity P294 FINISHED
Object Mbini
Mbini is a town in mainland Equatorial Guinea situated along the Benito River, known historically as a small river port and local administrative center.
 E207846 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: Mbini | Statement: [Río Muni, containsCity, Mbini]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mbini
Context triple: [Río Muni, containsCity, Mbini]
  • A. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • B. Mvita
    Mvita is an alternative name for Kimvita, a historic Swahili settlement and cultural center on the coast of present-day Kenya.
  • C. Kumba
    Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
  • D. Mwotlap
    Mwotlap is an Oceanic Austronesian language spoken on Mota Lava and nearby islands in northern Vanuatu.
  • 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: Mbini
Triple: [Río Muni, containsCity, Mbini]
Generated description
Mbini is a town in mainland Equatorial Guinea situated along the Benito River, known historically as a small river port and local administrative center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mbini
Target entity description: Mbini is a town in mainland Equatorial Guinea situated along the Benito River, known historically as a small river port and local administrative center.
  • A. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • B. Mvita
    Mvita is an alternative name for Kimvita, a historic Swahili settlement and cultural center on the coast of present-day Kenya.
  • C. Kumba
    Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
  • D. Mwotlap
    Mwotlap is an Oceanic Austronesian language spoken on Mota Lava and nearby islands in northern Vanuatu.
  • 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_69a88600b2f88190bc09303e68ab517e completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69abb0b6ac108190921c197abc5ab5b5 completed March 7, 2026, 4:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69add1d8dd8881909189029a047bc2b4 completed March 8, 2026, 7:45 p.m.
NEDg Description generation batch_69add28b804c8190a625e5d1405c59be completed March 8, 2026, 7:48 p.m.
NED2 Entity disambiguation (via description) batch_69add35731588190a13c969490ca2c09 completed March 8, 2026, 7:51 p.m.
Created at: March 4, 2026, 7:34 p.m.