Triple

T3746046
Position Surface form Disambiguated ID Type / Status
Subject Culver CityBus E81212 entity
Predicate hasRoute P4374 FINISHED
Object Line 3
Line 3 is a Culver CityBus route in the Los Angeles area that connects key destinations across Culver City and nearby communities.
E384342 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 | Statement: [Culver CityBus, hasRoute, Line 3]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Line 3
Context triple: [Culver CityBus, hasRoute, Line 3]
  • A. Line 3
    Line 3 is a route of Mexico City’s Metrobús bus rapid transit system that serves key corridors with dedicated lanes and high-capacity articulated buses.
  • B. Line 3
    Line 3 is a major rapid transit route of the Guangzhou Metro system, known for its high passenger volume and key role in connecting central urban areas with the airport and suburban districts.
  • C. Line 3
    Line 3 is a major north–south route of the Tehran Metro system, connecting key residential and commercial areas across the city.
  • D. Line 3
    Line 3 is a major north–south rapid transit route of the Shanghai Metro system, known for its elevated tracks and extensive coverage across the city.
  • E. Line 3
    Line 3 is a rapid transit line of the Toronto subway system, commonly known as the Scarborough RT, that served the Scarborough district.
  • 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
Triple: [Culver CityBus, hasRoute, Line 3]
Generated description
Line 3 is a Culver CityBus route in the Los Angeles area that connects key destinations across Culver City and nearby communities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Line 3
Target entity description: Line 3 is a Culver CityBus route in the Los Angeles area that connects key destinations across Culver City and nearby communities.
  • A. Line 3
    Line 3 is a route of Mexico City’s Metrobús bus rapid transit system that serves key corridors with dedicated lanes and high-capacity articulated buses.
  • B. Line 3
    Line 3 is a major trolleybus route within Geneva’s public transport system, connecting key districts of the city.
  • C. 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.
  • D. Line 3
    Line 3 is one of the main lines of the Mexico City Metro system, running in a generally north–south direction and serving several key residential and commercial areas.
  • E. Line 3
    Line 3 is a major north–south route of the Tehran Metro system, connecting key residential and commercial areas across 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_69ad8b19b7b08190a6188804e99c53e9 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcb680ddc819094205beb342699f9 completed March 8, 2026, 7:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4db2c2c5081909b83d89c989a8d1c completed March 14, 2026, 3:51 a.m.
NEDg Description generation batch_69b4dbd0b6e88190a857afe3c1041788 completed March 14, 2026, 3:53 a.m.
NED2 Entity disambiguation (via description) batch_69b4dc5114ec8190aee92e21a48ae268 completed March 14, 2026, 3:56 a.m.
Created at: March 8, 2026, 3:35 p.m.