Triple
T15429068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Milan Metro Line 3 |
E369587
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object |
Line 3
Line 3 is a major line of the Milan Metro rapid transit system in Milan, Italy, connecting key areas of the city along a north–south axis.
|
E1156272
|
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: [Milan Metro Line 3, shortName, Line 3]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 3 Context triple: [Milan Metro Line 3, shortName, 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 future rapid transit route of the Seville Metro intended to extend and improve the city’s urban rail network.
-
D.
Line 3
Line 3 is a major line of the Saint Petersburg Metro system, serving as one of the city's primary rapid transit routes.
-
E.
Line 3
Line 3 is a rapid transit line of the Shijiazhuang Metro system in Shijiazhuang, Hebei, China.
- 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: [Milan Metro Line 3, shortName, Line 3]
Generated description
Line 3 is a major line of the Milan Metro rapid transit system in Milan, Italy, connecting key areas of the city along a north–south axis.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line 3 Target entity description: Line 3 is a major line of the Milan Metro rapid transit system in Milan, Italy, connecting key areas of the city along a north–south axis.
-
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 a major north–south route of the Seoul Metropolitan Subway system, connecting key residential and commercial districts across the city and into surrounding areas.
-
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 major rapid transit line of the Chongqing Metro system in Chongqing, China, known for its extensive elevated monorail route that connects key urban and suburban areas.
- 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_69d85a1849f48190bf898068b2806fae |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03ec31f4881908b26ff7c381d7bc9 |
completed | April 16, 2026, 1:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff1a827d9081909fabc48bc685ba5b |
completed | May 9, 2026, 11:29 a.m. |
| NEDg | Description generation | batch_69ff1b4c13e08190b2ccee59da02d0ae |
completed | May 9, 2026, 11:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff1bdb39b481908f0b1df595837bc4 |
completed | May 9, 2026, 11:34 a.m. |
Created at: April 10, 2026, 3:21 a.m.