Triple

T21292492
Position Surface form Disambiguated ID Type / Status
Subject La Mesa Reservoir E524830 entity
Predicate cityServed P82 FINISHED
Object Mandaluyong NE NERFINISHED

How this triple was built (2 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: Mandaluyong | Statement: [La Mesa Reservoir, cityServed, Mandaluyong]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mandaluyong
Context triple: [La Mesa Reservoir, cityServed, Mandaluyong]
  • A. Mandaluyong chosen
    Mandaluyong is a highly urbanized city in the Philippines known as part of Metro Manila’s central business and commercial district.
  • B. Lungsod ng Pasig
    Lungsod ng Pasig is a highly urbanized city in Metro Manila, Philippines, known as a major commercial and residential center that includes the Ortigas Center business district.
  • C. Muntinlupa
    Muntinlupa is a highly urbanized city in the southern part of Metro Manila in the Philippines, known for housing the New Bilibid Prison and major commercial and residential developments like Alabang.
  • D. Caloocan
    Caloocan is a highly urbanized city in the Philippines that forms part of the northern section of Metro Manila and serves as a major residential and commercial hub.
  • E. Malabon
    Malabon is a coastal city in the northern part of Metro Manila in the Philippines, known for its historic districts, flood-prone waterways, and distinctive local cuisine.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b517e6748190850d6f6ddf323d69 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e73855e5d08190aed5e285247b4e23 completed April 21, 2026, 8:41 a.m.
Created at: April 16, 2026, 4:04 p.m.