Triple
T10754784
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rouen tramway |
E253661
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object |
Line T3
Line T3 is a route of the Rouen tramway network in France, providing urban light-rail transit service within the Rouen metropolitan area.
|
E885295
|
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 T3 | Statement: [Rouen tramway, hasLine, Line T3]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line T3 Context triple: [Rouen tramway, hasLine, Line T3]
-
A.
Line T2
Line T2 is one of the tram lines serving the city of Rouen, France, as part of its urban light rail network.
-
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 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 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 T3 Triple: [Rouen tramway, hasLine, Line T3]
Generated description
Line T3 is a route of the Rouen tramway network in France, providing urban light-rail transit service within the Rouen metropolitan area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line T3 Target entity description: Line T3 is a route of the Rouen tramway network in France, providing urban light-rail transit service within the Rouen metropolitan area.
-
A.
Line T2
Line T2 is one of the tram lines serving the city of Rouen, France, as part of its urban light rail network.
-
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 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.
-
D.
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.
-
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
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_69d6aa5e51e8819095f06881cecf152e |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d72e9d0f688190a9be024929d2f960 |
completed | April 9, 2026, 4:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de55a12b8c8190bbeeb6d176f42b49 |
completed | April 14, 2026, 2:56 p.m. |
| NEDg | Description generation | batch_69de5952f6c48190abd3b87372d54f58 |
completed | April 14, 2026, 3:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69de5ed49c9c8190a4085407f88d7a05 |
completed | April 14, 2026, 3:35 p.m. |
Created at: April 8, 2026, 9:15 p.m.