Triple

T4335023
Position Surface form Disambiguated ID Type / Status
Subject Calabarzon E97442 entity
Predicate hasCity P316 FINISHED
Object Lipa
Lipa is a highly urbanized city in the province of Batangas in the Calabarzon region of the Philippines, known as a commercial, educational, and religious center.
 E430659 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: Lipa | Statement: [Calabarzon, hasCity, Lipa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lipa
Context triple: [Calabarzon, hasCity, Lipa]
  • A. Mwinilunga
    Mwinilunga is a town in northwestern Zambia known as an administrative and commercial center near the borders with Angola and the Democratic Republic of the Congo.
  • B. Kilembe
    Kilembe is a town in western Uganda that serves as a common starting point for treks to the Rwenzori Mountains, including ascents of Margherita Peak.
  • C. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • D. Mutombo
    Mutombo is a retired Congolese-American NBA Hall of Fame center renowned for his dominant shot-blocking, defensive prowess, and humanitarian work.
  • E. Apswa
    Apswa is the endonym used by the Abkhaz people to refer to themselves and their language.
  • 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: Lipa
Triple: [Calabarzon, hasCity, Lipa]
Generated description
Lipa is a highly urbanized city in the province of Batangas in the Calabarzon region of the Philippines, known as a commercial, educational, and religious center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lipa
Target entity description: Lipa is a highly urbanized city in the province of Batangas in the Calabarzon region of the Philippines, known as a commercial, educational, and religious center.
  • A. Mwinilunga
    Mwinilunga is a town in northwestern Zambia known as an administrative and commercial center near the borders with Angola and the Democratic Republic of the Congo.
  • B. Kilembe
    Kilembe is a town in western Uganda that serves as a common starting point for treks to the Rwenzori Mountains, including ascents of Margherita Peak.
  • C. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • D. Mutombo
    Mutombo is a retired Congolese-American NBA Hall of Fame center renowned for his dominant shot-blocking, defensive prowess, and humanitarian work.
  • E. Apswa
    Apswa is the endonym used by the Abkhaz people to refer to themselves and their language.
  • 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_69b3454662a481908fbcd0bbfaa3a0a4 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b35152bfc88190ab5d53ca38f98d8a completed March 12, 2026, 11:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5d0a9967481908828ceeb76ce4cbf completed March 14, 2026, 9:18 p.m.
NEDg Description generation batch_69b5d14748108190a6f5d4aebaa83ed6 completed March 14, 2026, 9:21 p.m.
NED2 Entity disambiguation (via description) batch_69b5d1b610888190bccace493224c373 completed March 14, 2026, 9:23 p.m.
Created at: March 12, 2026, 11:14 p.m.