Triple

T35912258
Position Surface form Disambiguated ID Type / Status
Subject Phra Ram 9 MRT station E1038647 entity
Predicate connectsTo P845 FINISHED
Object Central Rama 9 shopping mall
Central Rama 9 shopping mall is a major modern shopping and lifestyle complex in Bangkok, Thailand, featuring a wide range of retail stores, dining options, and entertainment facilities.
E2162204 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: Central Rama 9 shopping mall | Statement: [Phra Ram 9 MRT station, connectsTo, Central Rama 9 shopping mall]
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: Central Rama 9 shopping mall
Triple: [Phra Ram 9 MRT station, connectsTo, Central Rama 9 shopping mall]
Generated description
Central Rama 9 shopping mall is a major modern shopping and lifestyle complex in Bangkok, Thailand, featuring a wide range of retail stores, dining options, and entertainment facilities.

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_69f76e2259608190bf6788a132e0d139 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7aaa2525081909a333b254f7059c6 completed May 3, 2026, 8:05 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38ae2faecc819081260405036da5df completed June 22, 2026, 3:38 a.m.
NEDg Description generation batch_6a38b0474ab081909051e42e2ec5fd10 completed June 22, 2026, 3:47 a.m.
NED2 Entity disambiguation (via description) batch_6a38b0e7d9608190b1b4ba4464a662ce completed June 22, 2026, 3:50 a.m.
Created at: May 3, 2026, 4:07 p.m.