Triple

T9164001
Position Surface form Disambiguated ID Type / Status
Subject Muntinlupa E219900 entity
Predicate hasDistrict P459 FINISHED
Object Sucat
Sucat is a barangay in the city of Muntinlupa in Metro Manila, Philippines, known for its residential communities and proximity to major transport routes.
 E783129 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: Sucat | Statement: [Muntinlupa, hasDistrict, Sucat]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sucat
Context triple: [Muntinlupa, hasDistrict, Sucat]
  • A. Sulat
    Sulat is a coastal municipality in the province of Eastern Samar in the Philippines, known for its rural communities and Pacific shoreline.
  • B. Tayasal
    Tayasal was a major Itza Maya city in present-day Guatemala that served as one of the last independent Maya strongholds before Spanish conquest.
  • C. Sakia
    Sakia is a prominent cultural center and arts venue in Cairo, Egypt, known for hosting concerts, exhibitions, and a wide range of cultural events.
  • D. Kalabahi
    Kalabahi is the main town and administrative center on Alor Island in Indonesia’s East Nusa Tenggara province.
  • E. Balabac
    Balabac is a remote island municipality in the southernmost part of the Philippine province of Palawan, known for its rich marine biodiversity and pristine beaches.
  • 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: Sucat
Triple: [Muntinlupa, hasDistrict, Sucat]
Generated description
Sucat is a barangay in the city of Muntinlupa in Metro Manila, Philippines, known for its residential communities and proximity to major transport routes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sucat
Target entity description: Sucat is a barangay in the city of Muntinlupa in Metro Manila, Philippines, known for its residential communities and proximity to major transport routes.
  • A. Sulat
    Sulat is a coastal municipality in the province of Eastern Samar in the Philippines, known for its rural communities and Pacific shoreline.
  • B. Tayasal
    Tayasal was a major Itza Maya city in present-day Guatemala that served as one of the last independent Maya strongholds before Spanish conquest.
  • C. Sakia
    Sakia is a prominent cultural center and arts venue in Cairo, Egypt, known for hosting concerts, exhibitions, and a wide range of cultural events.
  • D. Kalabahi
    Kalabahi is the main town and administrative center on Alor Island in Indonesia’s East Nusa Tenggara province.
  • E. Balabac
    Balabac is a remote island municipality in the southernmost part of the Philippine province of Palawan, known for its rich marine biodiversity and pristine beaches.
  • 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_69ca83e3633c81908688a9fa2306ba99 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccaa2d6628819084ac4734650fe912 completed April 1, 2026, 5:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69d05c04533c81909d92ad5fff9bbadf completed April 4, 2026, 12:32 a.m.
NEDg Description generation batch_69d05da61590819093401c997e81e048 completed April 4, 2026, 12:39 a.m.
NED2 Entity disambiguation (via description) batch_69d05dfcea9c819083966d951b8cddd1 completed April 4, 2026, 12:40 a.m.
Created at: March 30, 2026, 7:21 p.m.