Triple

T7892281
Position Surface form Disambiguated ID Type / Status
Subject Mu Ko Lanta National Park E183264 entity
Predicate hasPart P35 FINISHED
Object Ko Talabeng
Ko Talabeng is a small, scenic island in Thailand known for its dramatic limestone cliffs, caves, and kayaking spots within the Mu Ko Lanta archipelago.
 E697327 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: Ko Talabeng | Statement: [Mu Ko Lanta National Park, hasPart, Ko Talabeng]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ko Talabeng
Context triple: [Mu Ko Lanta National Park, hasPart, Ko Talabeng]
  • A. Kuvinga
    Kuvinga is an alternative name for the Kuvi language, a Dravidian language spoken by indigenous communities in eastern India.
  • B. Kesbewa
    Kesbewa is a suburban town in Sri Lanka’s Western Province, situated within the greater Colombo metropolitan area.
  • C. Kasulu
    Kasulu is a town in western Tanzania that serves as one of the main urban and commercial centers of the Kigoma Region.
  • D. Tongelre
    Tongelre is a district in the Dutch city of Eindhoven, known for its mix of residential neighborhoods, green spaces, and former industrial areas.
  • E. Tongala
    Tongala is a small rural town in northern Victoria, Australia, known for its dairy industry and agricultural surroundings.
  • 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: Ko Talabeng
Triple: [Mu Ko Lanta National Park, hasPart, Ko Talabeng]
Generated description
Ko Talabeng is a small, scenic island in Thailand known for its dramatic limestone cliffs, caves, and kayaking spots within the Mu Ko Lanta archipelago.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ko Talabeng
Target entity description: Ko Talabeng is a small, scenic island in Thailand known for its dramatic limestone cliffs, caves, and kayaking spots within the Mu Ko Lanta archipelago.
  • A. Kuvinga
    Kuvinga is an alternative name for the Kuvi language, a Dravidian language spoken by indigenous communities in eastern India.
  • B. Kesbewa
    Kesbewa is a suburban town in Sri Lanka’s Western Province, situated within the greater Colombo metropolitan area.
  • C. Kasulu
    Kasulu is a town in western Tanzania that serves as one of the main urban and commercial centers of the Kigoma Region.
  • D. Tongelre
    Tongelre is a district in the Dutch city of Eindhoven, known for its mix of residential neighborhoods, green spaces, and former industrial areas.
  • E. Tongala
    Tongala is a small rural town in northern Victoria, Australia, known for its dairy industry and agricultural surroundings.
  • 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_69ca828c474c8190a254d6499871eaff completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb39fef2e48190a6282c217c33c57a completed March 31, 2026, 3:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5ba51ee48190b654a931da2c049f completed March 31, 2026, 5:29 a.m.
NEDg Description generation batch_69cb5f1e84fc8190b535016cb69405b4 completed March 31, 2026, 5:43 a.m.
NED2 Entity disambiguation (via description) batch_69cb76a214488190b90e5db28511daa0 completed March 31, 2026, 7:24 a.m.
Created at: March 30, 2026, 5 p.m.