Triple

T8907380
Position Surface form Disambiguated ID Type / Status
Subject Huntingdon railway station E212095 entity
Predicate stationCode P1289 FINISHED
Object HUN
HUN is the three-letter National Rail station code for Huntingdon railway station in Cambridgeshire, England.
 E766403 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: HUN | Statement: [Huntingdon railway station, stationCode, HUN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: HUN
Context triple: [Huntingdon railway station, stationCode, HUN]
  • A. HUN
    HUN is the three-letter International Olympic Committee country code used to represent Hungary in Olympic competitions and related events.
  • B. Hangu
    Hangu is a town in Pakistan’s Khyber Pakhtunkhwa province known for its strategic location and history of sectarian tensions.
  • C. Hu
    Hu is a common Chinese surname borne by many notable figures, including former Chinese president Hu Jintao.
  • D. Ungar
    Ungar is a surname of Germanic and Central European origin, historically associated with people from Hungary or of Hungarian descent.
  • E. Hannut
    Hannut is a municipality in the French-speaking Walloon Region of Belgium, known for its rural character and location between Liège and Brussels.
  • 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: HUN
Triple: [Huntingdon railway station, stationCode, HUN]
Generated description
HUN is the three-letter National Rail station code for Huntingdon railway station in Cambridgeshire, England.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: HUN
Target entity description: HUN is the three-letter National Rail station code for Huntingdon railway station in Cambridgeshire, England.
  • A. HUN
    HUN is the three-letter International Olympic Committee country code used to represent Hungary in Olympic competitions and related events.
  • B. Hangu
    Hangu is a town in Pakistan’s Khyber Pakhtunkhwa province known for its strategic location and history of sectarian tensions.
  • C. Hu
    Hu is a common Chinese surname borne by many notable figures, including former Chinese president Hu Jintao.
  • D. Ungar
    Ungar is a surname of Germanic and Central European origin, historically associated with people from Hungary or of Hungarian descent.
  • E. Hannut
    Hannut is a municipality in the French-speaking Walloon Region of Belgium, known for its rural character and location between Liège and Brussels.
  • 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_69ca839255248190b43984294abd92ae completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc64c6a87c81909331a39619f913c0 completed April 1, 2026, 12:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfba2cb5e48190813e9c08198149b0 completed April 3, 2026, 1:01 p.m.
NEDg Description generation batch_69cfbaf8aa4c8190821ace3f0f53a9b3 completed April 3, 2026, 1:04 p.m.
NED2 Entity disambiguation (via description) batch_69cfbb9585688190b3aa3d817bafba51 completed April 3, 2026, 1:07 p.m.
Created at: March 30, 2026, 6:55 p.m.