Triple

T18823463
Position Surface form Disambiguated ID Type / Status
Subject Santa Mesa E460322 entity
Predicate hasLandmark P105 FINISHED
Object V. Mapa station 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: V. Mapa station | Statement: [Santa Mesa, hasLandmark, V. Mapa station]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: V. Mapa station
Context triple: [Santa Mesa, hasLandmark, V. Mapa station]
  • A. V. Mapa station chosen
    V. Mapa station is an elevated Manila Light Rail Transit Line 2 station located in the Santa Mesa area of Manila, Philippines.
  • B. Obelya station
    Obelya station is a metro station in Sofia, Bulgaria, serving as an interchange point between lines of the Sofia Metro network.
  • C. Viau station
    Viau station is a Montreal Metro station on the Green Line, serving the Hochelaga-Maisonneuve area and providing access to nearby attractions such as the Olympic Park.
  • D. Ozerki station
    Ozerki station is a Saint Petersburg Metro station serving the Vyborgsky District in the northern part of the city.
  • E. Bolna Station
    Bolna Station is a remote railway stop on Norway’s Nordland Line, serving the mountainous Saltfjellet region just north of the Arctic Circle.
  • 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_69d8dcf94c288190a06dea029ae4b223 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5a6bce5588190bd0aefcd0c51edad completed April 20, 2026, 4:08 a.m.
Created at: April 10, 2026, 11:55 a.m.