Triple

T2329553
Position Surface form Disambiguated ID Type / Status
Subject Soccsksargen E48369 entity
Predicate hasProvincialComponent P34763 FINISHED
Object Sarangani
Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
 E257340 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: Sarangani | Statement: [Soccsksargen, hasProvincialComponent, Sarangani]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sarangani
Context triple: [Soccsksargen, hasProvincialComponent, Sarangani]
  • A. Balanga
    Balanga is a coastal city in the province of Bataan in the Philippines, situated along the shores of Manila Bay.
  • B. Surigaonon
    Surigaonon is a Visayan language spoken primarily in the Caraga region of northeastern Mindanao in the Philippines.
  • C. Talokan
    Talokan is a fictional underwater Mesoamerican-inspired kingdom ruled by Namor in the Marvel Cinematic Universe film "Black Panther: Wakanda Forever."
  • D. Dauin
    Dauin is a coastal municipality in Negros Oriental, Philippines, known for its rich marine biodiversity and popular dive sites, including access to the renowned Apo Island.
  • E. Aguiguan
    Aguiguan is a small, uninhabited island in the Northern Mariana Islands known for its rugged terrain and seabird colonies.
  • 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: Sarangani
Triple: [Soccsksargen, hasProvincialComponent, Sarangani]
Generated description
Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sarangani
Target entity description: Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
  • A. Balanga
    Balanga is a coastal city in the province of Bataan in the Philippines, situated along the shores of Manila Bay.
  • B. Surigaonon
    Surigaonon is a Visayan language spoken primarily in the Caraga region of northeastern Mindanao in the Philippines.
  • C. Talokan
    Talokan is a fictional underwater Mesoamerican-inspired kingdom ruled by Namor in the Marvel Cinematic Universe film "Black Panther: Wakanda Forever."
  • D. Dauin
    Dauin is a coastal municipality in Negros Oriental, Philippines, known for its rich marine biodiversity and popular dive sites, including access to the renowned Apo Island.
  • E. Aguiguan
    Aguiguan is a small, uninhabited island in the Northern Mariana Islands known for its rugged terrain and seabird colonies.
  • 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_69a88aa308a88190b0b86c011fda7fce completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abd0d6b0e48190aee9131ca182e52f completed March 7, 2026, 7:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae96185a1c8190a115588f8a5b92f7 completed March 9, 2026, 9:42 a.m.
NEDg Description generation batch_69ae96ea1b6c81908e05ca78c5e07320 completed March 9, 2026, 9:46 a.m.
NED2 Entity disambiguation (via description) batch_69ae97a775008190bdda2f63810cd5e8 completed March 9, 2026, 9:49 a.m.
Created at: March 4, 2026, 7:50 p.m.