Triple

T33620759
Position Surface form Disambiguated ID Type / Status
Subject Roman Catholic Diocese of Parañaque E861260 entity
Predicate territoryIncludes P285 FINISHED
Object City of Parañaque
The City of Parañaque is a highly urbanized coastal city in Metro Manila, Philippines, known for its bustling commercial districts, entertainment complexes, and proximity to Ninoy Aquino International Airport.
E2150661 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: City of Parañaque | Statement: [Roman Catholic Diocese of Parañaque, territoryIncludes, City of Parañaque]
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: City of Parañaque
Triple: [Roman Catholic Diocese of Parañaque, territoryIncludes, City of Parañaque]
Generated description
The City of Parañaque is a highly urbanized coastal city in Metro Manila, Philippines, known for its bustling commercial districts, entertainment complexes, and proximity to Ninoy Aquino International Airport.

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_69f34980fabc81909819228729a9ca84 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6f81b8dc48190abf63b5f5cb8538a completed May 3, 2026, 7:24 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3872607284819086f49208a9338bf1 completed June 21, 2026, 11:23 p.m.
NEDg Description generation batch_6a3872dec92c819086f638084f5f72ae completed June 21, 2026, 11:25 p.m.
NED2 Entity disambiguation (via description) batch_6a387377a850819080349b2f0c461bc6 completed June 21, 2026, 11:27 p.m.
Created at: May 1, 2026, 1:41 a.m.