Triple

T16990702
Position Surface form Disambiguated ID Type / Status
Subject Farnborough North railway station E412186 entity
Predicate stationCode P1289 FINISHED
Object FNN
FNN is the three-letter National Rail station code assigned to Farnborough North railway station in Hampshire, England.
 E1244417 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: FNN | Statement: [Farnborough North railway station, stationCode, FNN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: FNN
Context triple: [Farnborough North railway station, stationCode, FNN]
  • A. NN
    NN is the postcode area in the United Kingdom that covers Northampton and surrounding parts of Northamptonshire.
  • B. FCN
    FCN is the common abbreviation for 1. FC Nürnberg, a German football club based in Nuremberg.
  • C. deep feedforward networks
    Deep feedforward networks are a class of neural network architectures in which information flows in one direction through multiple layers to learn complex input–output mappings without recurrent connections.
  • D. BNNS
    BNNS (Basic Neural Network Subroutines) is Apple’s low-level, hardware-accelerated framework for performing neural network and machine learning computations efficiently on Apple devices.
  • E. ANN
    ANN is the National Rail station code for Annan railway station in Dumfries and Galloway, Scotland.
  • 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: FNN
Triple: [Farnborough North railway station, stationCode, FNN]
Generated description
FNN is the three-letter National Rail station code assigned to Farnborough North railway station in Hampshire, England.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: FNN
Target entity description: FNN is the three-letter National Rail station code assigned to Farnborough North railway station in Hampshire, England.
  • A. NN
    NN is the postcode area in the United Kingdom that covers Northampton and surrounding parts of Northamptonshire.
  • B. FCN
    FCN is the common abbreviation for 1. FC Nürnberg, a German football club based in Nuremberg.
  • C. deep feedforward networks
    Deep feedforward networks are a class of neural network architectures in which information flows in one direction through multiple layers to learn complex input–output mappings without recurrent connections.
  • D. BNNS
    BNNS (Basic Neural Network Subroutines) is Apple’s low-level, hardware-accelerated framework for performing neural network and machine learning computations efficiently on Apple devices.
  • E. ANN
    ANN is the National Rail station code for Annan railway station in Dumfries and Galloway, Scotland.
  • 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_69d886cb581c8190ab05f4b429c9cd85 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d27fbaa0819099f79fc74d211647 completed April 18, 2026, 6:50 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00dc14d5688190945f7ae72f724922 completed May 10, 2026, 7:27 p.m.
NEDg Description generation batch_6a0114d5aeb0819086f1a5d279ac0d0f completed May 10, 2026, 11:29 p.m.
NED2 Entity disambiguation (via description) batch_6a0115c583608190bf07ac205399f253 completed May 10, 2026, 11:33 p.m.
Created at: April 10, 2026, 5:32 a.m.