Triple
T7949470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Milano Cadorna station |
E184577
|
entity |
| Predicate | hasMetroLine |
P17559
|
FINISHED |
| Object |
Line 1
Line 1 is a major Milan Metro line that serves key areas of the city, including Milano Cadorna station.
|
E704598
|
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 1 | Statement: [Milano Cadorna station, hasMetroLine, Line 1]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 1 Context triple: [Milano Cadorna station, hasMetroLine, Line 1]
-
A.
Line 1
Line 1 is the oldest and one of the busiest lines of the Santiago Metro, running primarily east–west across central Santiago, Chile.
-
B.
Line 1
Line 1 is one of the main east–west rapid transit lines of the Beijing Subway, serving as a core corridor through central Beijing.
-
C.
Line 1
Line 1 is the oldest and one of the busiest lines of the Mexico City Metro, running east–west across the city and serving many central, high-traffic stations.
-
D.
Line 1
Line 1 is a major rapid transit line of the Guangzhou Metro system in Guangzhou, China, serving as one of the city's primary east–west corridors.
-
E.
Line 1
Line 1 is a major north–south rapid transit line of the Shanghai Metro and one of the system’s oldest and busiest routes.
- 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 1 Triple: [Milano Cadorna station, hasMetroLine, Line 1]
Generated description
Line 1 is a major Milan Metro line that serves key areas of the city, including Milano Cadorna station.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line 1 Target entity description: Line 1 is a major Milan Metro line that serves key areas of the city, including Milano Cadorna station.
-
A.
Line 1
Line 1 is the first and main automated metro line of the Turin Metro system in Turin, Italy, connecting key areas of the city along an underground route.
-
B.
Line 1
Line 1 is one of the main lines of the Barcelona Metro rapid transit system, running on a largely east–west axis and serving several key districts of the city.
-
C.
Line 1
Line 1 is a major north–south rapid transit line of the Shanghai Metro and one of the system’s oldest and busiest routes.
-
D.
Line 1
Line 1 is the oldest and one of the busiest lines of the Paris Métro, running primarily east–west through central Paris and serving many major landmarks.
-
E.
Line 1
Line 1 is the first and main rapid transit line of the Seville Metro system in Seville, Spain, connecting key districts across the metropolitan area.
- 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_69ca8292cba881908a64427b938dac47 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3b2d09a4819097aa49e29a5426ec |
completed | March 31, 2026, 3:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbe03c7d308190aec1172415be995c |
completed | March 31, 2026, 2:54 p.m. |
| NEDg | Description generation | batch_69cbe4383d0c819085e7c95e7b0be16e |
completed | March 31, 2026, 3:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cc34a83cec81908aba7afbaea53449 |
completed | March 31, 2026, 8:55 p.m. |
Created at: March 30, 2026, 5:10 p.m.