Triple

T16289414
Position Surface form Disambiguated ID Type / Status
Subject Roxas E395477 entity
Predicate hasToponymicUse P20238 FINISHED
Object Roxas, Isabela
Roxas, Isabela is a municipality in the province of Isabela in the Philippines, known for its agricultural economy and rural communities.
 E308468 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: Roxas, Isabela | Statement: [Roxas, hasToponymicUse, Roxas, Isabela]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Roxas, Isabela
Context triple: [Roxas, hasToponymicUse, Roxas, Isabela]
  • A. Delos Reyes
    Delos Reyes is a Spanish-derived surname commonly found in the Philippines and other Spanish-influenced regions.
  • B. Isabela
    Isabela is a large agricultural province in the Cagayan Valley region of the Philippines, known especially for its extensive rice and corn production.
  • C. Isabela
    Isabela is a coastal municipality in northwestern Puerto Rico known for its beaches, surfing spots, and scenic Atlantic shoreline.
  • D. Palau Aguilar
    Palau Aguilar is a historic medieval palace in Barcelona’s Gothic Quarter that serves as the main building of the Picasso Museum.
  • E. Cabrero
    Cabrero is a small Chilean city located in the Biobío Region, known for its agricultural activities and role as a local transport and services hub.
  • 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: Roxas, Isabela
Triple: [Roxas, hasToponymicUse, Roxas, Isabela]
Generated description
Roxas, Isabela is a municipality in the province of Isabela in the Philippines, known for its agricultural economy and rural communities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Roxas, Isabela
Target entity description: Roxas, Isabela is a municipality in the province of Isabela in the Philippines, known for its agricultural economy and rural communities.
  • A. Delos Reyes
    Delos Reyes is a Spanish-derived surname commonly found in the Philippines and other Spanish-influenced regions.
  • B. Isabela
    Isabela is a coastal municipality in northwestern Puerto Rico known for its beaches, surfing spots, and scenic Atlantic shoreline.
  • C. Isabela chosen
    Isabela is a large agricultural province in the Cagayan Valley region of the Philippines, known especially for its extensive rice and corn production.
  • D. Palau Aguilar
    Palau Aguilar is a historic medieval palace in Barcelona’s Gothic Quarter that serves as the main building of the Picasso Museum.
  • E. Cabrero
    Cabrero is a small Chilean city located in the Biobío Region, known for its agricultural activities and role as a local transport and services hub.
  • F. None of above.

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_69d87f22c7248190a54c949738441e2e completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e249175e24819082e571039e278056 completed April 17, 2026, 2:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a001f94ede48190835e8a0c6f5d0f19 completed May 10, 2026, 6:03 a.m.
NEDg Description generation batch_6a002067aa708190bc2583c95ab133a4 completed May 10, 2026, 6:06 a.m.
NED2 Entity disambiguation (via description) batch_6a00214a2a908190a11388a63de1f7af completed May 10, 2026, 6:10 a.m.
Created at: April 10, 2026, 5:05 a.m.