Triple

T855122
Position Surface form Disambiguated ID Type / Status
Subject Głogów E18473 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object DGL
DGL is the vehicle registration code assigned to the town of Głogów in Poland.
 E100964 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: DGL | Statement: [Głogów, vehicleRegistrationCode, DGL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DGL
Context triple: [Głogów, vehicleRegistrationCode, DGL]
  • A. DAG
    DAG is the National Rail station code for Dalgety Bay railway station in Fife, Scotland.
  • B. SGD
    SGD is the official currency code for the Singapore dollar, the national currency of Singapore used in domestic and international transactions.
  • C. DGC
    DGC is the United Nations Department of Global Communications, responsible for promoting global awareness and understanding of the UN’s work through strategic communication and public outreach.
  • D. DRL
    DRL is the U.S. State Department bureau responsible for promoting democracy, protecting human rights, and advancing labor rights worldwide.
  • E. TensorFlow
    TensorFlow is an open-source, end-to-end machine learning and deep learning framework widely used for building, training, and deploying neural network models at scale.
  • 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: DGL
Triple: [Głogów, vehicleRegistrationCode, DGL]
Generated description
DGL is the vehicle registration code assigned to the town of Głogów in Poland.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: DGL
Target entity description: DGL is the vehicle registration code assigned to the town of Głogów in Poland.
  • A. DAG
    DAG is the National Rail station code for Dalgety Bay railway station in Fife, Scotland.
  • B. SGD
    SGD is the official currency code for the Singapore dollar, the national currency of Singapore used in domestic and international transactions.
  • C. DGC
    DGC is the United Nations Department of Global Communications, responsible for promoting global awareness and understanding of the UN’s work through strategic communication and public outreach.
  • D. DRL
    DRL is the U.S. State Department bureau responsible for promoting democracy, protecting human rights, and advancing labor rights worldwide.
  • E. TensorFlow
    TensorFlow is an open-source, end-to-end machine learning and deep learning framework widely used for building, training, and deploying neural network models at scale.
  • 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_69a4938bdd3c8190a954a3c11844d9cf completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4ac3a48c08190b4677d825fcbfaf9 completed March 1, 2026, 9:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7a3bfcf308190b1ffc63ccd32cc66 completed March 4, 2026, 3:15 a.m.
NEDg Description generation batch_69a7a4416144819099d6388fac05f475 completed March 4, 2026, 3:17 a.m.
NED2 Entity disambiguation (via description) batch_69a7a4b346b88190a264742a3f6ab2d1 completed March 4, 2026, 3:19 a.m.
Created at: March 1, 2026, 7:39 p.m.