Triple

T2367130
Position Surface form Disambiguated ID Type / Status
Subject San Lázaro E46007 entity
Predicate metroStationCode P29273 FINISHED
Object LB-SL
LB-SL is the station code used to identify the San Lázaro metro station within its urban rail network.
 E261447 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: LB-SL | Statement: [San Lázaro, metroStationCode, LB-SL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LB-SL
Context triple: [San Lázaro, metroStationCode, LB-SL]
  • A. LB
    LB is the two-letter ISO 3166-1 alpha-2 country code representing Lebanon.
  • B. SL1
    SL1 is a Boston bus rapid transit route on the MBTA Silver Line that connects downtown with Logan International Airport.
  • C. SL3
    SL3 is a branch of Boston’s MBTA Silver Line bus rapid transit system that connects South Station with Chelsea via the Seaport and East Boston tunnels.
  • D. SL2
    SL2 is a branch of Boston’s MBTA Silver Line bus rapid transit service that connects South Station with the Seaport and Design Center area.
  • E. SL
    The Mercedes-Benz SL is a long-running line of luxury grand touring roadsters renowned for combining high performance with elegant design and advanced technology.
  • 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: LB-SL
Triple: [San Lázaro, metroStationCode, LB-SL]
Generated description
LB-SL is the station code used to identify the San Lázaro metro station within its urban rail network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LB-SL
Target entity description: LB-SL is the station code used to identify the San Lázaro metro station within its urban rail network.
  • A. LB
    LB is the two-letter ISO 3166-1 alpha-2 country code representing Lebanon.
  • B. SL1
    SL1 is a Boston bus rapid transit route on the MBTA Silver Line that connects downtown with Logan International Airport.
  • C. SL3
    SL3 is a branch of Boston’s MBTA Silver Line bus rapid transit system that connects South Station with Chelsea via the Seaport and East Boston tunnels.
  • D. SL2
    SL2 is a branch of Boston’s MBTA Silver Line bus rapid transit service that connects South Station with the Seaport and Design Center area.
  • E. SL
    SL is a German vehicle registration code used on license plates to identify cars registered in the Saalekreis district of Saxony-Anhalt.
  • 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_69a88a145268819083e2736cb835c696 completed March 4, 2026, 7:37 p.m.
NER Named-entity recognition batch_69abd0d813dc8190aa331cdca0b75eca completed March 7, 2026, 7:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69aea89ae4688190be2e0825f0875ed3 completed March 9, 2026, 11:01 a.m.
NEDg Description generation batch_69aeac4b9b108190b440c991342df9b6 completed March 9, 2026, 11:17 a.m.
NED2 Entity disambiguation (via description) batch_69aeacc8445481908a3ae8bd62493413 completed March 9, 2026, 11:19 a.m.
Created at: March 4, 2026, 7:56 p.m.