Triple

T4108002
Position Surface form Disambiguated ID Type / Status
Subject Nueva Ecija E88501 entity
Predicate hasCity P316 FINISHED
Object San Jose City
San Jose City is a landlocked component city in the province of Nueva Ecija in the Philippines, known as an agricultural and commercial hub in Central Luzon.
 E455196 NE FINISHED

How this triple was built (4 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: San Jose City | Statement: [Nueva Ecija, hasCity, San Jose City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: San Jose City
Context triple: [Nueva Ecija, hasCity, San Jose City]
  • A. San Jose
    San Jose is the main town on the island of Tinian in the Northern Mariana Islands, serving as its administrative and population center.
  • B. San Jose
    San Jose is a coastal municipality in the Philippine province of Negros Oriental known for its rural communities and proximity to Dumaguete City.
  • C. San Jose
    San Jose is a major technology and innovation hub in Silicon Valley and one of the largest cities in Northern California.
  • D. San Jose
    San Jose is a coastal municipality in the province of Northern Samar in the Eastern Visayas region of the Philippines.
  • E. San Jose
    San Jose is a municipality in the province of Tarlac in the Central Luzon region of the Philippines, known for its predominantly agricultural economy.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: San Jose City
Triple: [Nueva Ecija, hasCity, San Jose City]
Generated description
San Jose City is a landlocked component city in the province of Nueva Ecija in the Philippines, known as an agricultural and commercial hub in Central Luzon.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: San Jose City
Target entity description: San Jose City is a landlocked component city in the province of Nueva Ecija in the Philippines, known as an agricultural and commercial hub in Central Luzon.
  • A. San Jose
    San Jose is a major technology and innovation hub in Silicon Valley and one of the largest cities in Northern California.
  • B. San Jose
    San Jose is the main town on the island of Tinian in the Northern Mariana Islands, serving as its administrative and population center.
  • C. San Jose
    San Jose is a coastal municipality in the Philippine province of Negros Oriental known for its rural communities and proximity to Dumaguete City.
  • D. San Jose
    San Jose is a coastal municipality in the province of Northern Samar in the Eastern Visayas region of the Philippines.
  • E. San Jose
    San Jose is a municipality in the province of Tarlac in the Central Luzon region of the Philippines, known for its predominantly agricultural economy.
  • F. None of above. chosen

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_69aed9484fb881909146f4c772ad277c completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af019e23c481909578eba1c9270282 completed March 9, 2026, 5:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69bde03b1f508190b9d5026103d3ee79 completed March 21, 2026, 12:03 a.m.
NEDg Description generation batch_69bde26ff3ac8190abada7fe7e03a5e2 completed March 21, 2026, 12:12 a.m.
NED2 Entity disambiguation (via description) batch_69bde3191b448190ad2affaa30bb1515 completed March 21, 2026, 12:15 a.m.
Created at: March 9, 2026, 3:40 p.m.