Triple

T8147871
Position Surface form Disambiguated ID Type / Status
Subject Erzgebirgskreis E190258 entity
Predicate hasVehicleRegistrationCode P1173 FINISHED
Object ZP
ZP is a German vehicle registration code assigned to the Erzgebirgskreis district in the state of Saxony.
 E717343 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: ZP | Statement: [Erzgebirgskreis, hasVehicleRegistrationCode, ZP]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ZP
Context triple: [Erzgebirgskreis, hasVehicleRegistrationCode, ZP]
  • A. PZ
    PZ is the vehicle registration code used on license plates for vehicles registered in the Preveza regional unit of Greece.
  • B. PZ
    PZ is the IATA airline designator assigned to LATAM Airlines Paraguay, the Paraguayan branch of the LATAM Airlines Group.
  • C. ZS
    ZS is the vehicle registration code assigned to cars registered in the Polish city of Szczecin.
  • D. ZS
    ZS is the stock ticker symbol for Zscaler, a cloud-based information security company traded on the NASDAQ.
  • E. ZPO
    ZPO is the standard abbreviation for the German Code of Civil Procedure, the central statute governing civil court proceedings in Germany.
  • 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: ZP
Triple: [Erzgebirgskreis, hasVehicleRegistrationCode, ZP]
Generated description
ZP is a German vehicle registration code assigned to the Erzgebirgskreis district in the state of Saxony.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ZP
Target entity description: ZP is a German vehicle registration code assigned to the Erzgebirgskreis district in the state of Saxony.
  • A. PZ
    PZ is the vehicle registration code used on license plates for vehicles registered in the Preveza regional unit of Greece.
  • B. PZ
    PZ is the IATA airline designator assigned to LATAM Airlines Paraguay, the Paraguayan branch of the LATAM Airlines Group.
  • C. ZS
    ZS is the vehicle registration code assigned to cars registered in the Polish city of Szczecin.
  • D. ZS
    ZS is the stock ticker symbol for Zscaler, a cloud-based information security company traded on the NASDAQ.
  • E. ZPO
    ZPO is the standard abbreviation for the German Code of Civil Procedure, the central statute governing civil court proceedings in Germany.
  • 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_69ca82be7ba8819087de0147e9292c83 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb447e74e081908df774edb2134209 completed March 31, 2026, 3:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69ccbee697208190a1d9c98b2a4414bd completed April 1, 2026, 6:44 a.m.
NEDg Description generation batch_69ccc30f1fc48190991e0caa9ea6e735 completed April 1, 2026, 7:02 a.m.
NED2 Entity disambiguation (via description) batch_69ccd8041c00819094094701ace21aa0 completed April 1, 2026, 8:32 a.m.
Created at: March 30, 2026, 5:36 p.m.