Triple

T37168753
Position Surface form Disambiguated ID Type / Status
Subject Gaysorn Village E920855 entity
Predicate hasPart P35 FINISHED
Object Gaysorn Tower
Gaysorn Tower is a premium mixed-use office and retail skyscraper in central Bangkok, Thailand, known for its upscale facilities and integration with the Gaysorn Village complex.
E2215833 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: Gaysorn Tower | Statement: [Gaysorn Village, hasPart, Gaysorn Tower]
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: Gaysorn Tower
Triple: [Gaysorn Village, hasPart, Gaysorn Tower]
Generated description
Gaysorn Tower is a premium mixed-use office and retail skyscraper in central Bangkok, Thailand, known for its upscale facilities and integration with the Gaysorn Village complex.

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_69f76ea16f288190b445aa1604d996f4 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fb35c891308190a2892ce3c1e375f1 completed May 6, 2026, 12:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a402bbf194881908fcf20156abdbb3a completed June 27, 2026, 7:59 p.m.
NEDg Description generation batch_6a402e11ca90819091b7bbc444aeb33e completed June 27, 2026, 8:09 p.m.
NED2 Entity disambiguation (via description) batch_6a402fa5d5bc81908c72f41f35c123e9 completed June 27, 2026, 8:16 p.m.
Created at: May 3, 2026, 4:15 p.m.