Triple

T5838829
Position Surface form Disambiguated ID Type / Status
Subject Zope E129541 entity
Predicate license P181 FINISHED
Object ZPL
ZPL is the Zope Public License, an open-source software license used primarily for the Zope application server and related Python projects.
 E551822 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: [Zope, license, ZPL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ZPL
Context triple: [Zope, license, ZPL]
  • A. Dymo
    Dymo is a brand best known for its label makers and labeling solutions used in offices, homes, and industrial settings.
  • B. ZLP
    ZLP is the IATA station code for Zürich Hauptbahnhof, the main railway station in Zurich, Switzerland.
  • C. Videojet
    Videojet is a leading manufacturer of industrial coding and marking solutions, including inkjet and laser printers used for product identification and packaging.
  • D. ZWL
    ZWL is the currency code for the reintroduced Zimbabwean dollar used in Zimbabwe’s monetary system.
  • E. ZHP
    ZHP is the National Rail station code assigned to Hyde Park Corner Underground station in London.
  • 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: [Zope, license, ZPL]
Generated description
ZPL is the Zope Public License, an open-source software license used primarily for the Zope application server and related Python projects.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ZPL
Target entity description: ZPL is the Zope Public License, an open-source software license used primarily for the Zope application server and related Python projects.
  • A. Dymo
    Dymo is a brand best known for its label makers and labeling solutions used in offices, homes, and industrial settings.
  • B. ZLP
    ZLP is the IATA station code for Zürich Hauptbahnhof, the main railway station in Zurich, Switzerland.
  • C. Videojet
    Videojet is a leading manufacturer of industrial coding and marking solutions, including inkjet and laser printers used for product identification and packaging.
  • D. ZWL
    ZWL is the currency code for the reintroduced Zimbabwean dollar used in Zimbabwe’s monetary system.
  • E. ZHP
    ZHP is the National Rail station code assigned to Hyde Park Corner Underground station in London.
  • 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_69c0084af79c81908af128ccc29983d0 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c034a852f88190a5d2c4b24ee17491 completed March 22, 2026, 6:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0a19e4ec4819099fa5c6fe9a6a257 completed March 23, 2026, 2:12 a.m.
NEDg Description generation batch_69c0a572f52481908fc4f2a833fd8edf completed March 23, 2026, 2:29 a.m.
NED2 Entity disambiguation (via description) batch_69c0a5d17d5c8190a5fe816d29400894 completed March 23, 2026, 2:30 a.m.
Created at: March 22, 2026, 3:54 p.m.