Triple

T1736060
Position Surface form Disambiguated ID Type / Status
Subject Transilien E37920 entity
Predicate safetySystem P840 FINISHED
Object KVB
KVB is a French train protection and automatic speed control system used to enhance the safety of railway operations.
 E194420 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: KVB | Statement: [Transilien, safetySystem, KVB]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: KVB
Context triple: [Transilien, safetySystem, KVB]
  • A. KCB
    KCB is the post-nominal abbreviation for Knight Commander of the Order of the Bath, a senior British order of chivalry.
  • B. VKO
    VKO is the IATA airport code for Vnukovo International Airport, one of Moscow’s major international airports in Russia.
  • C. National Bank of Kazakhstan
    The National Bank of Kazakhstan is the country's central bank, responsible for monetary policy, financial stability, and issuance of the national currency, the tenge.
  • D. UKB
    UKB is the IATA airport code for Kobe Airport, a regional airport serving the city of Kobe in Japan.
  • E. GVB
    GVB is Amsterdam’s primary public transport company, operating the city’s trams, buses, metro, and ferries.
  • 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: KVB
Triple: [Transilien, safetySystem, KVB]
Generated description
KVB is a French train protection and automatic speed control system used to enhance the safety of railway operations.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: KVB
Target entity description: KVB is a French train protection and automatic speed control system used to enhance the safety of railway operations.
  • A. KCB
    KCB is the post-nominal abbreviation for Knight Commander of the Order of the Bath, a senior British order of chivalry.
  • B. VKO
    VKO is the IATA airport code for Vnukovo International Airport, one of Moscow’s major international airports in Russia.
  • C. National Bank of Kazakhstan
    The National Bank of Kazakhstan is the country's central bank, responsible for monetary policy, financial stability, and issuance of the national currency, the tenge.
  • D. UKB
    UKB is the IATA airport code for Kobe Airport, a regional airport serving the city of Kobe in Japan.
  • E. GVB
    GVB is Amsterdam’s primary public transport company, operating the city’s trams, buses, metro, and ferries.
  • 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_69a8861cc6ac8190ac0b2e31ccf62851 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa63a369048190bae352573f5082f1 completed March 6, 2026, 5:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad8b008b7881909ac568af010bcf99 completed March 8, 2026, 2:43 p.m.
NEDg Description generation batch_69ad979dc1dc81908b64e57298ae6017 completed March 8, 2026, 3:37 p.m.
NED2 Entity disambiguation (via description) batch_69ad9836f4c8819098ba033b5f0d2a33 completed March 8, 2026, 3:39 p.m.
Created at: March 4, 2026, 7:30 p.m.