Triple

T11194720
Position Surface form Disambiguated ID Type / Status
Subject Gießen E264892 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object GI
GI is the vehicle registration code used on license plates for the German city of Gießen in the state of Hesse.
 E910908 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: GI | Statement: [Gießen, vehicleRegistrationCode, GI]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: GI
Context triple: [Gießen, vehicleRegistrationCode, GI]
  • A. GIN
    GIN is the three-letter ISO 3166-1 alpha-3 country code assigned to the West African nation of Guinea.
  • B. GU
    GU is the two-letter ISO 3166 country code assigned to Guam, an unincorporated territory of the United States in the western Pacific Ocean.
  • C. GU
    GU is a United Kingdom postcode area covering Guildford and surrounding parts of Surrey and nearby counties.
  • D. GU
    GU is an alternative name or abbreviation for the Gated Recurrent Unit, a type of recurrent neural network architecture used in deep learning for sequence modeling tasks.
  • E. GD
    GD is the stock ticker symbol for General Dynamics, a major American aerospace and defense corporation.
  • 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: GI
Triple: [Gießen, vehicleRegistrationCode, GI]
Generated description
GI is the vehicle registration code used on license plates for the German city of Gießen in the state of Hesse.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: GI
Target entity description: GI is the vehicle registration code used on license plates for the German city of Gießen in the state of Hesse.
  • A. GIN
    GIN is the three-letter ISO 3166-1 alpha-3 country code assigned to the West African nation of Guinea.
  • B. GU
    GU is the two-letter ISO 3166 country code assigned to Guam, an unincorporated territory of the United States in the western Pacific Ocean.
  • C. GU
    GU is a United Kingdom postcode area covering Guildford and surrounding parts of Surrey and nearby counties.
  • D. GU
    GU is an alternative name or abbreviation for the Gated Recurrent Unit, a type of recurrent neural network architecture used in deep learning for sequence modeling tasks.
  • E. GD
    GD is the stock ticker symbol for General Dynamics, a major American aerospace and defense corporation.
  • 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_69d6aa9eb9248190b20211772621b4bc completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e8bf14e481908563b15790af4d20 completed April 9, 2026, 5:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69e483f8ecf4819086f0bab3ca9ddcb4 completed April 19, 2026, 7:27 a.m.
NEDg Description generation batch_69e4878971cc8190aaa1fe32b32925a8 completed April 19, 2026, 7:43 a.m.
NED2 Entity disambiguation (via description) batch_69e4890e8ab881909c85593067041994 completed April 19, 2026, 7:49 a.m.
Created at: April 8, 2026, 9:29 p.m.