Triple

T34826767
Position Surface form Disambiguated ID Type / Status
Subject Gold Line (Bangkok) E1003945 entity
Predicate serves P98 FINISHED
Object Iconsiam shopping complex
Iconsiam shopping complex is a massive luxury riverside retail and lifestyle destination in Bangkok, featuring high-end shops, dining, entertainment, and cultural attractions along the Chao Phraya River.
E2113421 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: Iconsiam shopping complex | Statement: [Gold Line (Bangkok), serves, Iconsiam shopping complex]
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: Iconsiam shopping complex
Triple: [Gold Line (Bangkok), serves, Iconsiam shopping complex]
Generated description
Iconsiam shopping complex is a massive luxury riverside retail and lifestyle destination in Bangkok, featuring high-end shops, dining, entertainment, and cultural attractions along the Chao Phraya River.

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_69f76db7d1b4819093bd4912d80d845d completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f77ae20a34819085205e54e0241df6 completed May 3, 2026, 4:42 p.m.
NED1 Entity disambiguation (via context triple) batch_6a376fbf5bec81909d073cbf535cef89 completed June 21, 2026, 4:59 a.m.
NEDg Description generation batch_6a37703823ac81908261228f65fcfa4b completed June 21, 2026, 5:01 a.m.
NED2 Entity disambiguation (via description) batch_6a37716ab2c48190b7c24792201885c8 completed June 21, 2026, 5:06 a.m.
Created at: May 3, 2026, 4 p.m.