Triple

T10556908
Position Surface form Disambiguated ID Type / Status
Subject Wola tram depot E249109 entity
Predicate locatedInDistrict P40 FINISHED
Object Wola
Wola is a central district of Warsaw, Poland, known for its industrial heritage, residential neighborhoods, and significant role in the city's history.
 E904996 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: Wola | Statement: [Wola tram depot, locatedInDistrict, Wola]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wola
Context triple: [Wola tram depot, locatedInDistrict, Wola]
  • A. Wola Okrzejska
    Wola Okrzejska is a village in eastern Poland best known as the birthplace of Nobel Prize–winning novelist Henryk Sienkiewicz.
  • B. Mokotów
    Mokotów is a large, centrally located district of Warsaw known for its residential neighborhoods, parks, and business centers.
  • C. Wołosate
    Wołosate is a small village in southeastern Poland’s Bieszczady Mountains, known as a remote hiking base and the terminus of the Main Beskid Trail.
  • D. Bielany
    Bielany is a northern district of Warsaw, Poland, known for its residential neighborhoods, green spaces, and connection to the city center via the Warsaw Metro.
  • E. Okęcie
    Okęcie is a district in Warsaw, Poland, best known for hosting the city’s main international airport and various aviation-related facilities.
  • 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: Wola
Triple: [Wola tram depot, locatedInDistrict, Wola]
Generated description
Wola is a central district of Warsaw, Poland, known for its industrial heritage, residential neighborhoods, and significant role in the city's history.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wola
Target entity description: Wola is a central district of Warsaw, Poland, known for its industrial heritage, residential neighborhoods, and significant role in the city's history.
  • A. Wola Okrzejska
    Wola Okrzejska is a village in eastern Poland best known as the birthplace of Nobel Prize–winning novelist Henryk Sienkiewicz.
  • B. Mokotów
    Mokotów is a large, centrally located district of Warsaw known for its residential neighborhoods, parks, and business centers.
  • C. Wołosate
    Wołosate is a small village in southeastern Poland’s Bieszczady Mountains, known as a remote hiking base and the terminus of the Main Beskid Trail.
  • D. Bielany
    Bielany is a northern district of Warsaw, Poland, known for its residential neighborhoods, green spaces, and connection to the city center via the Warsaw Metro.
  • E. Okęcie
    Okęcie is a district in Warsaw, Poland, best known for hosting the city’s main international airport and various aviation-related facilities.
  • 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_69d381c733c08190ab1dd6239f5f34ae completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d52713eaa48190936b4b15e2c7e827 completed April 7, 2026, 3:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69e42d448fac81909137b0a0ed9b976e completed April 19, 2026, 1:17 a.m.
NEDg Description generation batch_69e42e67724481908bd9e73487a80d44 completed April 19, 2026, 1:22 a.m.
NED2 Entity disambiguation (via description) batch_69e4308103c48190b32ee3047d9a0860 completed April 19, 2026, 1:31 a.m.
Created at: April 6, 2026, 12:35 p.m.