Triple
T11870688
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metro Line 9 |
E282398
|
entity |
| Predicate | connectsWithLine |
P845
|
FINISHED |
| Object |
Line 3
Line 3 is a metro line that intersects with Metro Line 9 within the same urban rail network, serving as one of its connecting routes.
|
E953244
|
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: [Metro Line 9, connectsWithLine, Line 3]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 3 Context triple: [Metro Line 9, connectsWithLine, Line 3]
-
A.
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.
-
B.
Line 3
Line 3 is a major line of the Moscow Metro system, known for serving central Moscow and connecting key residential and commercial districts.
-
C.
Line 3
Line 3 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving key urban districts along a major north–south corridor.
-
D.
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.
-
E.
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.
- 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: [Metro Line 9, connectsWithLine, Line 3]
Generated description
Line 3 is a metro line that intersects with Metro Line 9 within the same urban rail network, serving as one of its connecting routes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line 3 Target entity description: Line 3 is a metro line that intersects with Metro Line 9 within the same urban rail network, serving as one of its connecting routes.
-
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 rapid transit route of the STC Metro system, serving key districts along its corridor.
-
C.
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.
-
D.
Line 3
Line 3 is a major north–south route of the Tehran Metro system, connecting key residential and commercial areas across the city.
-
E.
Line 3
Line 3 is a major line of the Moscow Metro system, known for serving central Moscow and connecting key residential and commercial districts.
- 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_69d6ab2945d081908a5851c916cbcfb5 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8be15fb2481908f514781ce2c617f |
completed | April 10, 2026, 9:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f417bb131c8190b0923e077cca74be |
completed | May 1, 2026, 3:02 a.m. |
| NEDg | Description generation | batch_69f41f8d297c81908cfe60b10989e550 |
completed | May 1, 2026, 3:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f422778a10819093bc2473ef30fe71 |
completed | May 1, 2026, 3:48 a.m. |
Created at: April 8, 2026, 9:43 p.m.