Triple

T21414252
Position Surface form Disambiguated ID Type / Status
Subject Police E528258 entity
Predicate carPlates P1173 FINISHED
Object ZPL
ZPL is a vehicle registration plate code used on police cars.
 E1483236 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: ZPL | Statement: [Police, carPlates, ZPL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ZPL
Context triple: [Police, carPlates, ZPL]
  • A. ZPL
    ZPL is the Zope Public License, an open-source software license used primarily for the Zope application server and related Python projects.
  • B. Dymo
    Dymo is a brand best known for its label makers and labeling solutions used in offices, homes, and industrial settings.
  • C. ZLP
    ZLP is the IATA station code for Zürich Hauptbahnhof, the main railway station in Zurich, Switzerland.
  • D. ZP
    ZP is a German vehicle registration code assigned to the Erzgebirgskreis district in the state of Saxony.
  • E. Videojet
    Videojet is a leading manufacturer of industrial coding and marking solutions, including inkjet and laser printers used for product identification and packaging.
  • 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: ZPL
Triple: [Police, carPlates, ZPL]
Generated description
ZPL is a vehicle registration plate code used on police cars.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ZPL
Target entity description: ZPL is a vehicle registration plate code used on police cars.
  • A. ZPL
    ZPL is the Zope Public License, an open-source software license used primarily for the Zope application server and related Python projects.
  • B. Dymo
    Dymo is a brand best known for its label makers and labeling solutions used in offices, homes, and industrial settings.
  • C. ZLP
    ZLP is the IATA station code for Zürich Hauptbahnhof, the main railway station in Zurich, Switzerland.
  • D. ZP
    ZP is a German vehicle registration code assigned to the Erzgebirgskreis district in the state of Saxony.
  • E. Videojet
    Videojet is a leading manufacturer of industrial coding and marking solutions, including inkjet and laser printers used for product identification and packaging.
  • 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_69e0c454c248819093425d1099101c09 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69e8b2032fe48190907b282e2fffa2bd completed April 22, 2026, 11:33 a.m.
NED1 Entity disambiguation (via context triple) batch_6a09c29cc1b08190836cc0c0723564ae completed May 17, 2026, 1:29 p.m.
NEDg Description generation batch_6a09c34f94108190bc5a5814c40c3550 completed May 17, 2026, 1:31 p.m.
NED2 Entity disambiguation (via description) batch_6a09c3d49b8c8190b5a138cb0530ca70 completed May 17, 2026, 1:34 p.m.
Created at: April 16, 2026, 5:45 p.m.