Triple
T17427371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charleroi Metro |
E423773
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object |
Line M2
Line M2 is a route of the Charleroi Metro system in Belgium, serving as part of the city's light rail rapid transit network.
|
E1268734
|
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 M2 | Statement: [Charleroi Metro, hasLine, Line M2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line M2 Context triple: [Charleroi Metro, hasLine, Line M2]
-
A.
Line M1
Line M1 is a primary metro line of the Warsaw Metro system, running north–south through the city and serving key residential and commercial districts.
-
B.
Line M1
Line M1 is a light metro route within the Charleroi Metro system in Belgium, serving as one of its primary urban transit lines.
-
C.
Line M
Line M is a cable car line within the Medellín Metro system that serves hillside neighborhoods by connecting them to the city’s main mass transit network.
-
D.
Line 2
Line 2 is one of the principal lines of the Mexico City Metro system, running across key central and western areas of the city and serving as a major high-capacity transit corridor.
-
E.
Line 2
Line 2 is a planned second rapid transit line of the Turin Metro system in Turin, Italy, intended to expand the city's urban rail network.
- 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 M2 Triple: [Charleroi Metro, hasLine, Line M2]
Generated description
Line M2 is a route of the Charleroi Metro system in Belgium, serving as part of the city's light rail rapid transit network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line M2 Target entity description: Line M2 is a route of the Charleroi Metro system in Belgium, serving as part of the city's light rail rapid transit network.
-
A.
Line M1
Line M1 is a primary metro line of the Warsaw Metro system, running north–south through the city and serving key residential and commercial districts.
-
B.
Line M1
Line M1 is a light metro route within the Charleroi Metro system in Belgium, serving as one of its primary urban transit lines.
-
C.
Line M
Line M is a cable car line within the Medellín Metro system that serves hillside neighborhoods by connecting them to the city’s main mass transit network.
-
D.
Line 2
Line 2 is one of the principal lines of the Mexico City Metro system, running across key central and western areas of the city and serving as a major high-capacity transit corridor.
-
E.
Line 2
Line 2 is a planned second rapid transit line of the Turin Metro system in Turin, Italy, intended to expand the city's urban rail network.
- 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_69d889d88b6081908bada047f5b3ba51 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e448fdc1348190985db52c8c74c394 |
completed | April 19, 2026, 3:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01afed3cb0819081fac1f1e59434f4 |
completed | May 11, 2026, 10:31 a.m. |
| NEDg | Description generation | batch_6a01b0b04bf48190813232c7be3d37e7 |
completed | May 11, 2026, 10:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01b1581cfc81908d724a8b324e698e |
completed | May 11, 2026, 10:37 a.m. |
Created at: April 10, 2026, 5:46 a.m.