Triple

T16089931
Position Surface form Disambiguated ID Type / Status
Subject Prague trams E390334 entity
Predicate partOf P40 FINISHED
Object PID
PID is Prague’s integrated public transport system that coordinates trams, buses, metro, and suburban services across the city and surrounding region.
 E1192884 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: PID | Statement: [Prague trams, partOf, PID]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: PID
Context triple: [Prague trams, partOf, PID]
  • A. PIP
    PIP is a UK welfare benefit that helps disabled people or those with long-term health conditions cover the extra costs of daily living and mobility.
  • B. PIP
    PIP is a Puerto Rican political party that advocates for the island’s full independence from the United States.
  • C. PI
    PI is the vehicle registration code used on license plates for the district of Pinneberg in the German state of Schleswig-Holstein.
  • D. PI
    PI is a leading independent research institute in Waterloo, Canada, dedicated to advancing fundamental theoretical physics and fostering scientific collaboration and education.
  • E. PIA
    PIA is the ICAO airline designator for Pakistan International Airlines, the national flag carrier of Pakistan.
  • 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: PID
Triple: [Prague trams, partOf, PID]
Generated description
PID is Prague’s integrated public transport system that coordinates trams, buses, metro, and suburban services across the city and surrounding region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: PID
Target entity description: PID is Prague’s integrated public transport system that coordinates trams, buses, metro, and suburban services across the city and surrounding region.
  • A. PIP
    PIP is a UK welfare benefit that helps disabled people or those with long-term health conditions cover the extra costs of daily living and mobility.
  • B. PIP
    PIP is a Puerto Rican political party that advocates for the island’s full independence from the United States.
  • C. PI
    PI is the vehicle registration code used on license plates for the district of Pinneberg in the German state of Schleswig-Holstein.
  • D. PI
    PI is a leading independent research institute in Waterloo, Canada, dedicated to advancing fundamental theoretical physics and fostering scientific collaboration and education.
  • E. PIA
    PIA is the ICAO airline designator for Pakistan International Airlines, the national flag carrier of Pakistan.
  • 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_69d87f198bc48190a8b7e53ca15b7ead completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e184522b2c8190986daae6cb2d9db4 completed April 17, 2026, 12:52 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffe490d494819081f812811f032702 completed May 10, 2026, 1:51 a.m.
NEDg Description generation batch_69ffe63f757c81908c7dc3c5ae3075c6 completed May 10, 2026, 1:58 a.m.
NED2 Entity disambiguation (via description) batch_69ffe6b3f25481908dd4b6108b5d95c0 completed May 10, 2026, 2 a.m.
Created at: April 10, 2026, 4:59 a.m.