Triple

T13260596
Position Surface form Disambiguated ID Type / Status
Subject Vermont/Santa Monica E315781 entity
Predicate hasStationCode P1289 FINISHED
Object VTSA
VTSA is the station code assigned to the Vermont/Santa Monica station on the Los Angeles Metro system.
E1030070 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: VTSA | Statement: [Vermont/Santa Monica, hasStationCode, VTSA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: VTSA
Context triple: [Vermont/Santa Monica, hasStationCode, VTSA]
  • A. VTST
    VTST is the station code for the Vermont/Sunset station on the Los Angeles Metro Rail system.
  • B. VTSG
    VTSG is the ICAO airport code for Krabi International Airport in Krabi, Thailand.
  • C. VTSP
    VTSP is the ICAO airport code for Phuket International Airport, a major international gateway to the island of Phuket in Thailand.
  • D. VETZ
    VETZ is the ICAO airport code assigned to Tezpur Airport in Assam, India.
  • E. V_ts
    V_ts is an element of the Cabibbo–Kobayashi–Maskawa (CKM) quark mixing matrix that quantifies the weak-interaction coupling between top and strange quarks in the Standard Model of particle physics.
  • 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: VTSA
Triple: [Vermont/Santa Monica, hasStationCode, VTSA]
Generated description
VTSA is the station code assigned to the Vermont/Santa Monica station on the Los Angeles Metro system.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: VTSA
Target entity description: VTSA is the station code assigned to the Vermont/Santa Monica station on the Los Angeles Metro system.
  • A. VTST
    VTST is the station code for the Vermont/Sunset station on the Los Angeles Metro Rail system.
  • B. VTSG
    VTSG is the ICAO airport code for Krabi International Airport in Krabi, Thailand.
  • C. VTSP
    VTSP is the ICAO airport code for Phuket International Airport, a major international gateway to the island of Phuket in Thailand.
  • D. VETZ
    VETZ is the ICAO airport code assigned to Tezpur Airport in Assam, India.
  • E. V_ts
    V_ts is an element of the Cabibbo–Kobayashi–Maskawa (CKM) quark mixing matrix that quantifies the weak-interaction coupling between top and strange quarks in the Standard Model of particle physics.
  • 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_69d806b1d9ac8190852c5571d5bd5f0f completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98f778088819082b8a596c04bfe02 completed April 11, 2026, 12:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69f70a4612cc8190b45673a3994cee18 completed May 3, 2026, 8:41 a.m.
NEDg Description generation batch_69f70c42d5008190bcd6275054637448 completed May 3, 2026, 8:50 a.m.
NED2 Entity disambiguation (via description) batch_69f70ce60a7081908f9498fcfec98e90 completed May 3, 2026, 8:52 a.m.
Created at: April 9, 2026, 9:25 p.m.