Triple

T15503311
Position Surface form Disambiguated ID Type / Status
Subject Mulhouse tramway E379014 entity
Predicate hasColorCode P35451 FINISHED
Object Line 3: green
Line 3: green is a designated route of the Mulhouse tramway system, identified by its green color on maps and signage.
 E1160969 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: Line 3: green | Statement: [Mulhouse tramway, hasColorCode, Line 3: green]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Line 3: green
Context triple: [Mulhouse tramway, hasColorCode, Line 3: green]
  • A. Line 3
    Line 3 is one of the main lines of the Barcelona Metro system, running through central parts of the city and connecting several key stations and neighborhoods.
  • B. Line 3
    Line 3 is one of the main lines of the Paris Métro, running in an east–west direction across the city and serving several central districts.
  • C. Line 3
    Line 3 is a Culver CityBus route in the Los Angeles area that connects key destinations across Culver City and nearby communities.
  • D. Line 3
    Line 3 is a rapid transit line of the Hangzhou Metro system in Hangzhou, China, serving as part of the city's expanding urban rail network.
  • E. Line 3
    Line 3 is a major route within the Linz tramway network in Austria, providing urban public transport across key parts of the city.
  • 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: Line 3: green
Triple: [Mulhouse tramway, hasColorCode, Line 3: green]
Generated description
Line 3: green is a designated route of the Mulhouse tramway system, identified by its green color on maps and signage.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Line 3: green
Target entity description: Line 3: green is a designated route of the Mulhouse tramway system, identified by its green color on maps and signage.
  • A. Line 3
    Line 3 is one of the main lines of the Barcelona Metro system, running through central parts of the city and connecting several key stations and neighborhoods.
  • B. Line 3
    Line 3 is one of the main lines of the Paris Métro, running in an east–west direction across the city and serving several central districts.
  • C. Line 3
    Line 3 is a Culver CityBus route in the Los Angeles area that connects key destinations across Culver City and nearby communities.
  • D. Line 3
    Line 3 is a rapid transit line of the Hangzhou Metro system in Hangzhou, China, serving as part of the city's expanding urban rail network.
  • E. Line 3
    Line 3 is a major route within the Linz tramway network in Austria, providing urban public transport across key parts of the city.
  • 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_69d85cd53a7c819080f5b9042c4c199e completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03fcc5bb88190b8a9a81419a9a38b completed April 16, 2026, 1:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff3669f908819087162b1b8a4e4320 completed May 9, 2026, 1:28 p.m.
NEDg Description generation batch_69ff375856448190a61979dfff751f06 completed May 9, 2026, 1:32 p.m.
NED2 Entity disambiguation (via description) batch_69ff382f1bbc8190810d0d825430f9ea completed May 9, 2026, 1:35 p.m.
Created at: April 10, 2026, 3:54 a.m.