Triple
T15980549
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Snowdon station |
E387559
|
entity |
| Predicate | connectsLine |
P845
|
FINISHED |
| Object |
Blue Line
The Blue Line is a public transit route that serves Snowdon station as part of its network.
|
E1188438
|
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: Blue Line | Statement: [Snowdon station, connectsLine, Blue Line]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blue Line Context triple: [Snowdon station, connectsLine, Blue Line]
-
A.
Blue Line
The Blue Line is one of the main lines of the Lisbon Metro system, serving key central and northern areas of Portugal’s capital city.
-
B.
Blue Line
The Blue Line is a light rail route in the Dallas Area Rapid Transit (DART) system serving key neighborhoods and suburbs in the Dallas–Fort Worth metroplex.
-
C.
Blue Line
The Blue Line is one of the primary routes of the MetroLink light rail system serving the St. Louis metropolitan area.
-
D.
Blue Line
The Blue Line is a planned rapid transit corridor of Bengaluru’s Namma Metro network intended to expand connectivity across additional parts of the city.
-
E.
Blue Line
The Blue Line is one of the aerial cable car routes in La Paz–El Alto’s Mi Teleférico urban transit system, providing high-altitude public transportation across the Bolivian cities.
- 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: Blue Line Triple: [Snowdon station, connectsLine, Blue Line]
Generated description
The Blue Line is a public transit route that serves Snowdon station as part of its network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Blue Line Target entity description: The Blue Line is a public transit route that serves Snowdon station as part of its network.
-
A.
Blue Line
Blue Line is a light rail service route within Salt Lake City’s TRAX public transit system, connecting key areas of the metropolitan region.
-
B.
Blue Line
The Blue Line is one of the primary light rail transit routes in Calgary's CTrain system, serving key corridors across the city.
-
C.
Blue Line
The Blue Line is a light rail service in Pittsburgh's public transit system that connects downtown with several southern suburbs.
-
D.
Blue Line
The Blue Line is one of the primary heavy-rail transit routes in Atlanta’s MARTA system, running east–west across the metropolitan area and serving key urban and suburban stations.
-
E.
Blue Line
The Blue Line is one of the major corridors of the Delhi Metro rapid transit system, connecting key residential and commercial areas across Delhi and its neighboring regions.
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e157542cd88190832e7ae79bd38ffc |
completed | April 16, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffc3b717c88190b974a44470136ff2 |
completed | May 9, 2026, 11:31 p.m. |
| NEDg | Description generation | batch_69ffc5444c1c8190854de5575b9ec1c5 |
completed | May 9, 2026, 11:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffc5b95290819098b28c44c22b2799 |
completed | May 9, 2026, 11:39 p.m. |
Created at: April 10, 2026, 4:54 a.m.