Triple

T6838725
Position Surface form Disambiguated ID Type / Status
Subject Avignon TGV station E157515 entity
Predicate hasIataCode P2569 FINISHED
Object XZN
XZN is the IATA station code assigned to the Avignon TGV high-speed railway station in southeastern France.
 E622756 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: XZN | Statement: [Avignon TGV station, hasIataCode, XZN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: XZN
Context triple: [Avignon TGV station, hasIataCode, XZN]
  • A. JXN
    JXN is the Amtrak station code for the passenger rail station in Jackson, Michigan.
  • B. XU
    XU was a clandestine Norwegian intelligence organization that gathered and transmitted vital information to the Allies during the German occupation in World War II.
  • C. XNM
    XNM is the IATA airport-style code assigned to Norwich railway station in Norwich, England, for use in integrated transport and ticketing systems.
  • D. ZWN
    ZWN is a former currency code used to denote an early version of the Zimbabwean dollar in international financial and foreign exchange contexts.
  • E. CN-XJ
    CN-XJ is the ISO 3166-2 code representing the Xinjiang Uyghur Autonomous Region in northwest China.
  • 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: XZN
Triple: [Avignon TGV station, hasIataCode, XZN]
Generated description
XZN is the IATA station code assigned to the Avignon TGV high-speed railway station in southeastern France.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: XZN
Target entity description: XZN is the IATA station code assigned to the Avignon TGV high-speed railway station in southeastern France.
  • A. JXN
    JXN is the Amtrak station code for the passenger rail station in Jackson, Michigan.
  • B. XU
    XU was a clandestine Norwegian intelligence organization that gathered and transmitted vital information to the Allies during the German occupation in World War II.
  • C. XNM
    XNM is the IATA airport-style code assigned to Norwich railway station in Norwich, England, for use in integrated transport and ticketing systems.
  • D. ZWN
    ZWN is a former currency code used to denote an early version of the Zimbabwean dollar in international financial and foreign exchange contexts.
  • E. CN-XJ
    CN-XJ is the ISO 3166-2 code representing the Xinjiang Uyghur Autonomous Region in northwest China.
  • 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_69c6882c53608190b99aebef079b23bd completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d67ee1c88190b82a9b6b3d1e3875 completed March 27, 2026, 7:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7240321108190860e91ebeb738a8f completed March 28, 2026, 12:42 a.m.
NEDg Description generation batch_69c724e915dc8190a82b69939f78420d completed March 28, 2026, 12:46 a.m.
NED2 Entity disambiguation (via description) batch_69c728ddadd881909c2faa435031a635 completed March 28, 2026, 1:03 a.m.
Created at: March 27, 2026, 2:19 p.m.