Triple

T1351991
Position Surface form Disambiguated ID Type / Status
Subject Vanuatu languages E28901 entity
Predicate includesLanguage P2177 FINISHED
Object Naman
Naman is an endangered Oceanic language spoken by a small community in Vanuatu.
 E155133 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: Naman | Statement: [Vanuatu languages, includesLanguage, Naman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Naman
Context triple: [Vanuatu languages, includesLanguage, Naman]
  • A. Samu
    Samu is a given name, commonly used as a short form or variant of Samuel in various cultures.
  • B. Guna
    Guna is a city in the central Indian state of Madhya Pradesh known as an important regional administrative and commercial center.
  • C. Namba
    Namba is a major commercial and entertainment district in Osaka, Japan, known for its bustling nightlife, shopping, and iconic neon-lit streets.
  • D. Jagaban
    Jagaban is the popular political nickname of Nigerian politician and current president Bola Ahmed Tinubu, often used to signify his influential “godfather” status in Nigerian politics.
  • E. Raman
    Raman is a common Indian surname most famously associated with physicist C. V. Raman, a Nobel laureate known for discovering the Raman effect in light scattering.
  • 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: Naman
Triple: [Vanuatu languages, includesLanguage, Naman]
Generated description
Naman is an endangered Oceanic language spoken by a small community in Vanuatu.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Naman
Target entity description: Naman is an endangered Oceanic language spoken by a small community in Vanuatu.
  • A. Samu
    Samu is a given name, commonly used as a short form or variant of Samuel in various cultures.
  • B. Guna
    Guna is a city in the central Indian state of Madhya Pradesh known as an important regional administrative and commercial center.
  • C. Namba
    Namba is a major commercial and entertainment district in Osaka, Japan, known for its bustling nightlife, shopping, and iconic neon-lit streets.
  • D. Jagaban
    Jagaban is the popular political nickname of Nigerian politician and current president Bola Ahmed Tinubu, often used to signify his influential “godfather” status in Nigerian politics.
  • E. Raman
    Raman is a common Indian surname most famously associated with physicist C. V. Raman, a Nobel laureate known for discovering the Raman effect in light scattering.
  • 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_69a498571d248190a0ac9eb02d97097f completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c26b1b4881908ae4b1b2c9b268a0 completed March 1, 2026, 10:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69acce6a1dd48190b17ac5a7cf3ab933 completed March 8, 2026, 1:18 a.m.
NEDg Description generation batch_69acceac84748190bbf9dd9c9ba5561f completed March 8, 2026, 1:19 a.m.
NED2 Entity disambiguation (via description) batch_69accf1778b081908936977cd9317774 completed March 8, 2026, 1:21 a.m.
Created at: March 1, 2026, 7:56 p.m.