Triple

T4636611
Position Surface form Disambiguated ID Type / Status
Subject Taal Lake E101546 entity
Predicate hasViewpoint P854 FINISHED
Object Tagaytay Ridge E363935 NE FINISHED

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: Tagaytay Ridge | Statement: [Taal Lake, hasViewpoint, Tagaytay Ridge]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tagaytay Ridge
Context triple: [Taal Lake, hasViewpoint, Tagaytay Ridge]
  • A. Batasan Hills
    Batasan Hills is a barangay in Quezon City, Metro Manila, known for housing the Philippine House of Representatives and other key government institutions.
  • B. Tagaytay chosen
    Tagaytay is a popular highland city in the Philippines known for its cool climate and scenic views of Taal Volcano and Taal Lake.
  • C. Mount Makiling
    Mount Makiling is a dormant volcanic mountain in Laguna, Philippines, known for its rich biodiversity, protected forest reserve, and prominence in local folklore and hiking tourism.
  • D. Osmeña Peak
    Osmeña Peak is a popular mountain and hiking destination in Cebu, Philippines, known for its jagged, picturesque peaks and panoramic views of the surrounding landscape.
  • E. Mount Nanlaud
    Mount Nanlaud is the tallest mountain on the Micronesian island of Pohnpei, known for its lush tropical rainforest and frequent cloud cover.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69bd43d2f1c081908cd4b7ec48ecc73d completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd5a62a9e48190b0cf1cbcc51f00c0 completed March 20, 2026, 2:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69be0368d2748190bc8560ecb16e1997 completed March 21, 2026, 2:33 a.m.
Created at: March 20, 2026, 1:13 p.m.