Triple

T27021208
Position Surface form Disambiguated ID Type / Status
Subject Kanchanaburi Province E680666 entity
Predicate hasSymbol P129 FINISHED
Object Krachiao flower (province flower)
The Krachiao flower is a distinctive wild Siam tulip that serves as the provincial flower of Kanchanaburi Province in Thailand.
E1750731 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: Krachiao flower (province flower) | Statement: [Kanchanaburi Province, hasSymbol, Krachiao flower (province flower)]
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: Krachiao flower (province flower)
Triple: [Kanchanaburi Province, hasSymbol, Krachiao flower (province flower)]
Generated description
The Krachiao flower is a distinctive wild Siam tulip that serves as the provincial flower of Kanchanaburi Province in Thailand.

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_69eeeb5450988190bfc9a3c012ac463a completed April 27, 2026, 4:51 a.m.
NER Named-entity recognition batch_69f622048e10819094ea97b9c638de15 completed May 2, 2026, 4:10 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1229cace008190aab8df5659f39468 completed May 23, 2026, 10:27 p.m.
NEDg Description generation batch_6a122a580e188190aeb20ac47d23e414 completed May 23, 2026, 10:29 p.m.
NED2 Entity disambiguation (via description) batch_6a122af21ba88190b6779cd1c12861a1 completed May 23, 2026, 10:32 p.m.
Created at: April 27, 2026, 7:08 a.m.