Triple

T34279711
Position Surface form Disambiguated ID Type / Status
Subject El Hogar Filipino Building E879558 entity
Predicate associatedWith P37 FINISHED
Object Escolta commercial district
The Escolta commercial district is a historic business and shopping area in Manila, Philippines, once known as the city’s premier financial and commercial hub during the early 20th century.
E2090237 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: Escolta commercial district | Statement: [El Hogar Filipino Building, associatedWith, Escolta commercial district]
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: Escolta commercial district
Triple: [El Hogar Filipino Building, associatedWith, Escolta commercial district]
Generated description
The Escolta commercial district is a historic business and shopping area in Manila, Philippines, once known as the city’s premier financial and commercial hub during the early 20th century.

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_69f349b5f6648190b9420d94a4cd16e0 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f712ee4d1081909fae80a3a2c7d32c completed May 3, 2026, 9:18 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36e629ba608190b3289611cf455612 completed June 20, 2026, 7:12 p.m.
NEDg Description generation batch_6a36e7c3a5d08190a90848fbc53f577a completed June 20, 2026, 7:19 p.m.
NED2 Entity disambiguation (via description) batch_6a36e8214050819088ce2baa27717cdf completed June 20, 2026, 7:21 p.m.
Created at: May 1, 2026, 1:57 a.m.