Triple

T5073987
Position Surface form Disambiguated ID Type / Status
Subject STC Metro E114346 entity
Predicate line P1293 FINISHED
Object Line 1 E59642 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: Line 1 | Statement: [STC Metro, line, Line 1]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Line 1
Context triple: [STC Metro, line, Line 1]
  • A. Line 1 chosen
    Line 1 is the oldest and one of the busiest lines of the Santiago Metro, running primarily east–west across central Santiago, Chile.
  • B. Line 1
    Line 1 is one of the main east–west rapid transit lines of the Beijing Subway, serving as a core corridor through central Beijing.
  • C. Line 1
    Line 1 is the oldest and one of the busiest lines of the Mexico City Metro, running east–west across the city and serving many central, high-traffic stations.
  • D. Line 1
    Line 1 is a major north–south rapid transit line of the Shanghai Metro and one of the system’s oldest and busiest routes.
  • E. Line 1
    Line 1 is the oldest and one of the busiest lines of the Paris Métro, running primarily east–west through central Paris and serving many major landmarks.
  • 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_69bd443cf28c8190ad371d603563dbdd completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd74d0be1c819081b26235fe602a30 completed March 20, 2026, 4:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69beb11b3f3c819089f09178f17940c5 completed March 21, 2026, 2:54 p.m.
Created at: March 20, 2026, 1:39 p.m.