Triple

T4048562
Position Surface form Disambiguated ID Type / Status
Subject Marienberg E84126 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object ERZ
ERZ is the vehicle registration code for the Erzgebirgskreis district in the Free State of Saxony, Germany.
 E408329 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: ERZ | Statement: [Marienberg, vehicleRegistrationCode, ERZ]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ERZ
Context triple: [Marienberg, vehicleRegistrationCode, ERZ]
  • A. ER3
    ER3 is the IATA aircraft type code used to designate the Embraer ERJ 135 regional jet.
  • B. ER
    ER is the commonly used abbreviation for United Russia, the dominant ruling political party in the Russian Federation.
  • C. ER
    ER is a critically acclaimed American medical drama television series that follows the personal and professional lives of staff in a busy Chicago emergency room.
  • D. ER
    ER is the vehicle registration code assigned to the German city of Erlangen in the state of Bavaria.
  • E. ERI
    ERI is the Earthquake Research Institute of the University of Tokyo, a leading Japanese center for seismology and earthquake-related research.
  • 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: ERZ
Triple: [Marienberg, vehicleRegistrationCode, ERZ]
Generated description
ERZ is the vehicle registration code for the Erzgebirgskreis district in the Free State of Saxony, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ERZ
Target entity description: ERZ is the vehicle registration code for the Erzgebirgskreis district in the Free State of Saxony, Germany.
  • A. ER3
    ER3 is the IATA aircraft type code used to designate the Embraer ERJ 135 regional jet.
  • B. ER
    ER is the vehicle registration code assigned to the German city of Erlangen in the state of Bavaria.
  • C. ER
    ER is the commonly used abbreviation for United Russia, the dominant ruling political party in the Russian Federation.
  • D. ER
    ER is a critically acclaimed American medical drama television series that follows the personal and professional lives of staff in a busy Chicago emergency room.
  • E. ERI
    ERI is the Earthquake Research Institute of the University of Tokyo, a leading Japanese center for seismology and earthquake-related research.
  • 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_69aed930bd5c819083e7dcc14fc44f69 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefb82d1a08190aa8c5c48d368b58b completed March 9, 2026, 4:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5565466648190802a9b8fd88c3572 completed March 14, 2026, 12:36 p.m.
NEDg Description generation batch_69b556cdd05081909fea1fb0a58bc7d1 completed March 14, 2026, 12:38 p.m.
NED2 Entity disambiguation (via description) batch_69b557d445d081908f48fe3bd06f786e completed March 14, 2026, 12:43 p.m.
Created at: March 9, 2026, 3:37 p.m.