Triple

T4132513
Position Surface form Disambiguated ID Type / Status
Subject Casablanca Tramway E85072 entity
Predicate hasLine P35 FINISHED
Object T1
T1 is one of the main tram lines in Casablanca’s urban light rail network, providing mass transit service across key districts of the city.
 E414798 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: T1 | Statement: [Casablanca Tramway, hasLine, T1]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: T1
Context triple: [Casablanca Tramway, hasLine, T1]
  • A. T1
    T1 is one of the tram routes of the Trambaix light rail network serving the Barcelona metropolitan area.
  • B. the T
    The T is the public transit system serving the Greater Boston area, operated by the Massachusetts Bay Transportation Authority.
  • C. T2
    T2 is a Sydney Trains suburban rail service designation used for the Inner West & Leppington Line in the Sydney metropolitan network.
  • D. T2
    T2 is a passenger terminal at Berlin Brandenburg Airport that handles check-in, security, and boarding operations for departing and arriving travelers.
  • E. T2
    T2 is the second passenger terminal at Chicago O'Hare International Airport, serving various domestic and regional airline operations.
  • 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: T1
Triple: [Casablanca Tramway, hasLine, T1]
Generated description
T1 is one of the main tram lines in Casablanca’s urban light rail network, providing mass transit service across key districts of the city.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: T1
Target entity description: T1 is one of the main tram lines in Casablanca’s urban light rail network, providing mass transit service across key districts of the city.
  • A. T1
    T1 is one of the tram routes of the Trambaix light rail network serving the Barcelona metropolitan area.
  • B. the T
    The T is the public transit system serving the Greater Boston area, operated by the Massachusetts Bay Transportation Authority.
  • C. T2
    T2 is a Sydney Trains suburban rail service designation used for the Inner West & Leppington Line in the Sydney metropolitan network.
  • D. T2
    T2 is the second passenger terminal at Chicago O'Hare International Airport, serving various domestic and regional airline operations.
  • E. T2
    T2 is a passenger terminal at Berlin Brandenburg Airport that handles check-in, security, and boarding operations for departing and arriving travelers.
  • 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_69aed935ccd881909dc61f81bcdb7a78 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af022f55fc81909f2a1a04d0ea59e6 completed March 9, 2026, 5:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69b576c26d4c81909b8be74855cbd03f completed March 14, 2026, 2:54 p.m.
NEDg Description generation batch_69b577c2b784819096d8218dd1c1478d completed March 14, 2026, 2:59 p.m.
NED2 Entity disambiguation (via description) batch_69b5782cc448819080e306952da24ac0 completed March 14, 2026, 3:01 p.m.
Created at: March 9, 2026, 3:42 p.m.