Triple

T11953730
Position Surface form Disambiguated ID Type / Status
Subject Surigao del Sur E284494 entity
Predicate hasMunicipality P847 FINISHED
Object Carmen
Carmen is a coastal municipality in the province of Surigao del Sur in the Caraga region of Mindanao, Philippines.
E956902 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: Carmen | Statement: [Surigao del Sur, hasMunicipality, Carmen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Carmen
Context triple: [Surigao del Sur, hasMunicipality, Carmen]
  • A. Carmen
    Carmen is a key character in the dark fantasy film "Pan’s Labyrinth," serving as the pregnant mother whose fragile health and marriage to a brutal captain frame the story’s wartime and familial tensions.
  • B. Carmen
    Carmen is a central district of San José, Costa Rica, known for its urban character and role in the capital’s administrative and commercial life.
  • C. Carmen
    Carmen is a supporting character in Jim Jarmusch’s film "Broken Flowers," connected to the protagonist’s journey to revisit women from his past.
  • D. Carmen
    Carmen is a landlocked municipality in the central part of Bohol Island in the Philippines, known for its proximity to the famous Chocolate Hills.
  • E. Carmen
    Carmen is a pivotal character in the 1986 film "The Color of Money," serving as the savvy and manipulative girlfriend-manager of young pool hustler Vincent Lauria.
  • 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: Carmen
Triple: [Surigao del Sur, hasMunicipality, Carmen]
Generated description
Carmen is a coastal municipality in the province of Surigao del Sur in the Caraga region of Mindanao, Philippines.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Carmen
Target entity description: Carmen is a coastal municipality in the province of Surigao del Sur in the Caraga region of Mindanao, Philippines.
  • A. Carmen
    Carmen is a municipality in the Philippine province of Agusan del Norte, located on the island of Mindanao.
  • B. Carmen
    Carmen is a landlocked agricultural municipality in the province of North Cotabato on the island of Mindanao in the Philippines.
  • C. Carmen
    Carmen is a municipality in the province of Cebu in the Philippines, known for its agricultural economy and proximity to coastal and upland attractions.
  • D. Carmen
    Carmen is a landlocked municipality in the central part of Bohol Island in the Philippines, known for its proximity to the famous Chocolate Hills.
  • E. Carmen
    Carmen is a central district of San José, Costa Rica, known for its urban character and role in the capital’s administrative and commercial life.
  • 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_69d6ab2db38c8190b1f0ed6663ef8ada completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d90365da288190a132703df563de23 completed April 10, 2026, 2:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69f4590584608190bc00840c43f115a0 completed May 1, 2026, 7:40 a.m.
NEDg Description generation batch_69f45f89d5b08190a87312d96e61898a completed May 1, 2026, 8:08 a.m.
NED2 Entity disambiguation (via description) batch_69f464a5191881908e291943996169cb completed May 1, 2026, 8:30 a.m.
Created at: April 8, 2026, 9:45 p.m.