Triple

T14940588
Position Surface form Disambiguated ID Type / Status
Subject Ngawi Regency E372513 entity
Predicate governedBy P46 FINISHED
Object Regent of Ngawi
The Regent of Ngawi is the chief local government leader and executive head of Ngawi Regency in Indonesia.
 E1129083 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: Regent of Ngawi | Statement: [Ngawi Regency, governedBy, Regent of Ngawi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Regent of Ngawi
Context triple: [Ngawi Regency, governedBy, Regent of Ngawi]
  • A. Regent of Sampang
    The Regent of Sampang is the chief local executive and political leader responsible for administering Sampang Regency in East Java, Indonesia.
  • B. Regent of Jepara
    The Regent of Jepara is the chief local government leader and executive head of the Jepara Regency in Central Java, Indonesia.
  • C. Regent of Klaten
    The Regent of Klaten is the chief local executive and political leader responsible for administering and overseeing government affairs in Indonesia’s Klaten Regency.
  • D. Regent of Jeneponto
    The Regent of Jeneponto is the chief local government leader and highest executive authority of Jeneponto Regency in South Sulawesi, Indonesia.
  • E. Regent of Tabanan
    The Regent of Tabanan is the chief local government leader and executive head of Tabanan Regency in Bali, Indonesia.
  • 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: Regent of Ngawi
Triple: [Ngawi Regency, governedBy, Regent of Ngawi]
Generated description
The Regent of Ngawi is the chief local government leader and executive head of Ngawi Regency in Indonesia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Regent of Ngawi
Target entity description: The Regent of Ngawi is the chief local government leader and executive head of Ngawi Regency in Indonesia.
  • A. Regent of Sampang
    The Regent of Sampang is the chief local executive and political leader responsible for administering Sampang Regency in East Java, Indonesia.
  • B. Regent of Jepara
    The Regent of Jepara is the chief local government leader and executive head of the Jepara Regency in Central Java, Indonesia.
  • C. Regent of Klaten
    The Regent of Klaten is the chief local executive and political leader responsible for administering and overseeing government affairs in Indonesia’s Klaten Regency.
  • D. Regent of Jeneponto
    The Regent of Jeneponto is the chief local government leader and highest executive authority of Jeneponto Regency in South Sulawesi, Indonesia.
  • E. Regent of Tabanan
    The Regent of Tabanan is the chief local government leader and executive head of Tabanan Regency in Bali, Indonesia.
  • 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_69d85cc9da0c81908d583ca3f63a3908 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded64a2f24819099b21566756668a2 completed April 15, 2026, 12:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe7e9078508190b5cbfe84125ba209 completed May 9, 2026, 12:23 a.m.
NEDg Description generation batch_69fe83361ad08190b98523c2d171a11e completed May 9, 2026, 12:43 a.m.
NED2 Entity disambiguation (via description) batch_69fe83f4a8b08190913d42808acf694d completed May 9, 2026, 12:46 a.m.
Created at: April 10, 2026, 2:38 a.m.