Triple

T3108224
Position Surface form Disambiguated ID Type / Status
Subject Kwara State E64885 entity
Predicate hasLocalGovernmentArea P8215 FINISHED
Object Edu
Edu is a local government area in Kwara State, Nigeria, known for its predominantly Nupe-speaking communities and agrarian economy.
 E326935 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: Edu | Statement: [Kwara State, hasLocalGovernmentArea, Edu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Edu
Context triple: [Kwara State, hasLocalGovernmentArea, Edu]
  • A. Edu
    Edu is a common shortened form of the given name Eduardo, often used as an informal nickname.
  • B. education commands
    Education commands are specialized military organizations responsible for overseeing and delivering training and professional development to service members within a larger training and education structure.
  • C. Ed
    Ed is a common masculine given name, typically used as a short form of names such as Edward, Edwin, or Edmund.
  • D. ED
    ED is the federal agency responsible for establishing policy, administering, and coordinating most education-related programs in the United States.
  • E. ED
    ED is a classic line-based text editor commonly used in Unix-like operating systems, known for its minimal interface and suitability for scripting and low-resource environments.
  • 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: Edu
Triple: [Kwara State, hasLocalGovernmentArea, Edu]
Generated description
Edu is a local government area in Kwara State, Nigeria, known for its predominantly Nupe-speaking communities and agrarian economy.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Edu
Target entity description: Edu is a local government area in Kwara State, Nigeria, known for its predominantly Nupe-speaking communities and agrarian economy.
  • A. Edu
    Edu is a common shortened form of the given name Eduardo, often used as an informal nickname.
  • B. education commands
    Education commands are specialized military organizations responsible for overseeing and delivering training and professional development to service members within a larger training and education structure.
  • C. Ed
    Ed is a common masculine given name, typically used as a short form of names such as Edward, Edwin, or Edmund.
  • D. ED
    ED is the federal agency responsible for establishing policy, administering, and coordinating most education-related programs in the United States.
  • E. ED
    ED is a classic line-based text editor commonly used in Unix-like operating systems, known for its minimal interface and suitability for scripting and low-resource environments.
  • 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_69ad857eeaf48190b34ebfdaa7a264cf completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada29eacc88190a19c5ca8e53e3dca completed March 8, 2026, 4:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2038c89248190b880108c82ad35b1 completed March 12, 2026, 12:06 a.m.
NEDg Description generation batch_69b2046f76488190adef6685544b080e completed March 12, 2026, 12:10 a.m.
NED2 Entity disambiguation (via description) batch_69b2054bca388190ad40b2303ac96373 completed March 12, 2026, 12:14 a.m.
Created at: March 8, 2026, 3:04 p.m.