Triple

T34285240
Position Surface form Disambiguated ID Type / Status
Subject SM Prime Holdings E879714 entity
Predicate notableProject P4 FINISHED
Object SM City Tianjin
SM City Tianjin is a large-scale shopping mall development in Tianjin, China, built and operated by Philippine-based mall developer SM Prime Holdings as part of its international expansion.
E1873706 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: SM City Tianjin | Statement: [SM Prime Holdings, notableProject, SM City Tianjin]
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: SM City Tianjin
Triple: [SM Prime Holdings, notableProject, SM City Tianjin]
Generated description
SM City Tianjin is a large-scale shopping mall development in Tianjin, China, built and operated by Philippine-based mall developer SM Prime Holdings as part of its international expansion.

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_69f349b6df1c81908e5e5b6c2ab6409b completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f7130d60c88190ab9682ba8c9fc7d8 completed May 3, 2026, 9:19 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36e62ff9d881909b608f8475188d2b completed June 20, 2026, 7:12 p.m.
NEDg Description generation batch_6a36f2221ce88190963ebdae39f691e1 completed June 20, 2026, 8:03 p.m.
NED2 Entity disambiguation (via description) batch_6a36f28e08c881909ea51ccaccb891a8 completed June 20, 2026, 8:05 p.m.
Created at: May 1, 2026, 1:57 a.m.