Triple

T8518460
Position Surface form Disambiguated ID Type / Status
Subject Mundari E201634 entity
Predicate hasDialect P4251 FINISHED
Object Kera
Kera is a regional dialect of the Mundari language spoken by sections of the Munda ethnic community in eastern India.
 E739582 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: Kera | Statement: [Mundari, hasDialect, Kera]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kera
Context triple: [Mundari, hasDialect, Kera]
  • A. Kerria
    Kerria is a small genus of deciduous flowering shrubs, best known for the ornamental Japanese kerria with its bright yellow, rose-like blooms.
  • B. Keratea
    Keratea is a town in eastern Attica, Greece, known for its historical significance and proximity to the Athens metropolitan area.
  • C. Kadina
    Kadina is a historic copper mining town and one of the main commercial centers on South Australia's Yorke Peninsula.
  • D. Tenea
    Tenea was an ancient Greek city, traditionally associated with Corinthian colonists and mythic Trojan origins, known from classical sources and archaeological discoveries in the Peloponnese.
  • E. Krorayina
    Krorayina is an ancient oasis city in the Tarim Basin of present-day Xinjiang, China, known for its role as a Silk Road trading center and its well-preserved archaeological remains.
  • 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: Kera
Triple: [Mundari, hasDialect, Kera]
Generated description
Kera is a regional dialect of the Mundari language spoken by sections of the Munda ethnic community in eastern India.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kera
Target entity description: Kera is a regional dialect of the Mundari language spoken by sections of the Munda ethnic community in eastern India.
  • A. Kerria
    Kerria is a small genus of deciduous flowering shrubs, best known for the ornamental Japanese kerria with its bright yellow, rose-like blooms.
  • B. Keratea
    Keratea is a town in eastern Attica, Greece, known for its historical significance and proximity to the Athens metropolitan area.
  • C. Kadina
    Kadina is a historic copper mining town and one of the main commercial centers on South Australia's Yorke Peninsula.
  • D. Tenea
    Tenea was an ancient Greek city, traditionally associated with Corinthian colonists and mythic Trojan origins, known from classical sources and archaeological discoveries in the Peloponnese.
  • E. Krorayina
    Krorayina is an ancient oasis city in the Tarim Basin of present-day Xinjiang, China, known for its role as a Silk Road trading center and its well-preserved archaeological remains.
  • 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_69ca8321bb44819081b74df0b710276d completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe626787c819087e72dd76b2d9310 completed March 31, 2026, 3:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce4e6c93d081909da2a748b0fa6fd3 completed April 2, 2026, 11:09 a.m.
NEDg Description generation batch_69ce4ffc30e08190b71e941d63d56015 completed April 2, 2026, 11:16 a.m.
NED2 Entity disambiguation (via description) batch_69ce54dc664081908ff63ec7f92834d7 completed April 2, 2026, 11:37 a.m.
Created at: March 30, 2026, 6:16 p.m.