Triple

T6099249
Position Surface form Disambiguated ID Type / Status
Subject Malaweg language E135952 entity
Predicate hasAlternativeName P39 FINISHED
Object Malaueg
Malaueg is an Austronesian language spoken by the Malaueg people in the northern Philippines, particularly in the province of Cagayan.
 E569545 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: Malaueg | Statement: [Malaweg language, hasAlternativeName, Malaueg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Malaueg
Context triple: [Malaweg language, hasAlternativeName, Malaueg]
  • A. Rødberg
    Rødberg is a small village in southern Norway that serves as the administrative center of Nore og Uvdal municipality and a local hub for hydroelectric power production.
  • B. Kvam
    Kvam is a municipality in Vestland county, western Norway, known for its scenic location along the Hardangerfjord and traditional fruit farming.
  • C. Mahlberg
    Mahlberg is a small town and municipality in the Ortenau district of Baden-Württemberg in southwestern Germany.
  • D. Økern
    Økern is a mixed residential and commercial neighborhood in Oslo, Norway, known for its shopping center, office developments, and transport connections.
  • E. Olesko
    Olesko is a historic town in western Ukraine best known for its medieval castle, which served as the birthplace of Polish King John III Sobieski.
  • 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: Malaueg
Triple: [Malaweg language, hasAlternativeName, Malaueg]
Generated description
Malaueg is an Austronesian language spoken by the Malaueg people in the northern Philippines, particularly in the province of Cagayan.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Malaueg
Target entity description: Malaueg is an Austronesian language spoken by the Malaueg people in the northern Philippines, particularly in the province of Cagayan.
  • A. Rødberg
    Rødberg is a small village in southern Norway that serves as the administrative center of Nore og Uvdal municipality and a local hub for hydroelectric power production.
  • B. Kvam
    Kvam is a municipality in Vestland county, western Norway, known for its scenic location along the Hardangerfjord and traditional fruit farming.
  • C. Mahlberg
    Mahlberg is a small town and municipality in the Ortenau district of Baden-Württemberg in southwestern Germany.
  • D. Økern
    Økern is a mixed residential and commercial neighborhood in Oslo, Norway, known for its shopping center, office developments, and transport connections.
  • E. Olesko
    Olesko is a historic town in western Ukraine best known for its medieval castle, which served as the birthplace of Polish King John III Sobieski.
  • 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_69c0087cd3c48190b459848c72d84eb1 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c05b3970808190ba90f5e4235db9f2 completed March 22, 2026, 9:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69c125475548819086b733a80056eba5 completed March 23, 2026, 11:34 a.m.
NEDg Description generation batch_69c128753cd8819096edb3c817bfae10 completed March 23, 2026, 11:48 a.m.
NED2 Entity disambiguation (via description) batch_69c129134ce08190ada54a7b3eda27f4 completed March 23, 2026, 11:50 a.m.
Created at: March 22, 2026, 4:13 p.m.