Triple

T27303932
Position Surface form Disambiguated ID Type / Status
Subject Kamphaeng Phet province E688996 entity
Predicate contains P35 FINISHED
Object Khlong Lan District
Khlong Lan District is a rural administrative district in central Thailand known for its mountainous terrain, waterfalls, and proximity to Khlong Lan National Park.
E1814324 NE FINISHED

How this triple was built (2 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: Khlong Lan District | Statement: [Kamphaeng Phet province, contains, Khlong Lan District]
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: Khlong Lan District
Triple: [Kamphaeng Phet province, contains, Khlong Lan District]
Generated description
Khlong Lan District is a rural administrative district in central Thailand known for its mountainous terrain, waterfalls, and proximity to Khlong Lan National Park.

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_69ef355b931c8190a63cafaf7bcc008b completed April 27, 2026, 10:07 a.m.
NER Named-entity recognition batch_69f627862bb8819091d51890051ddb97 completed May 2, 2026, 4:34 p.m.
NED1 Entity disambiguation (via context triple) batch_6a162782e8388190802b6d23889d0d47 completed May 26, 2026, 11:06 p.m.
NEDg Description generation batch_6a1628bdc5ac81909d5f7dbdfad7934c completed May 26, 2026, 11:11 p.m.
NED2 Entity disambiguation (via description) batch_6a16294f42508190aac4e3617131dafe completed May 26, 2026, 11:14 p.m.
Created at: April 27, 2026, 11:23 a.m.