Triple
T9581907
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mexico City Metrobús |
E231191
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object |
Line 1
Line 1 is the inaugural and one of the busiest routes of the Mexico City Metrobús bus rapid transit system, running along a major north–south corridor of the city.
|
E808672
|
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: [Mexico City Metrobús, hasLine, Line 1]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 1 Context triple: [Mexico City Metrobús, hasLine, 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 the oldest and one of the busiest lines of the Paris Métro, running primarily east–west through central Paris and serving many major landmarks.
-
C.
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.
-
D.
Line 1
Line 1 is the designation for the Red Line, one of the main rapid transit corridors of the Delhi Metro network in India.
-
E.
Line 1
Line 1 is a major Milan Metro line that serves key areas of the city, including Milano Cadorna station.
- 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: [Mexico City Metrobús, hasLine, Line 1]
Generated description
Line 1 is the inaugural and one of the busiest routes of the Mexico City Metrobús bus rapid transit system, running along a major north–south corridor of the city.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line 1 Target entity description: Line 1 is the inaugural and one of the busiest routes of the Mexico City Metrobús bus rapid transit system, running along a major north–south corridor of the city.
-
A.
Line 1
chosen
Line 1 is the inaugural corridor of the Mexico City Metrobús bus rapid transit system, serving as a major north–south transit route in the city.
-
B.
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.
-
C.
Line 1
Line 1 is the main north–south route of the Tehran Metro system, serving as one of its busiest and most important rapid transit lines.
-
D.
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.
-
E.
Line 1
Line 1 is the first and one of the main rapid transit lines of the Nanjing Metro system in Nanjing, China, connecting key urban areas along a north–south corridor.
- F. None of above.
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_69ca848161688190a68d514a0a9d5129 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd99cd59008190888eb11f00f61994 |
completed | April 1, 2026, 10:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1790fbfb88190b1d12f5ed3d4ef7e |
completed | April 4, 2026, 8:48 p.m. |
| NEDg | Description generation | batch_69d179a4006c81909e983559b5fd7698 |
completed | April 4, 2026, 8:50 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d17a3aebfc8190acbe62ac900dd9e7 |
completed | April 4, 2026, 8:53 p.m. |
Created at: March 30, 2026, 8:05 p.m.