Triple
T16089621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Florenc |
E390326
|
entity |
| Predicate | servesMetroLine |
P6301
|
FINISHED |
| Object |
Line B
Line B is one of the main lines of the Prague Metro system, running through central Prague and serving key transit hubs and residential districts.
|
E393164
|
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 B | Statement: [Florenc, servesMetroLine, Line B]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line B Context triple: [Florenc, servesMetroLine, Line B]
-
A.
Line B
Line B is one of the main tram routes in the Reims tramway network in Reims, France, providing urban public transport across key areas of the city.
-
B.
Line B
Line B is one of the main routes of the Strasbourg tramway network, serving key districts and connecting important transit hubs across the city.
-
C.
Line B
Line B is one of the main rapid transit lines of the Medellín Metro system, serving several neighborhoods in the Aburrá Valley metropolitan area.
-
D.
Line B
Line B is one of the main routes of the Porto Metro light rail system in Porto, Portugal, connecting key suburban areas with the city center.
-
E.
Line B
Line B is one of the main routes of the Rotterdam Metro rapid transit system, serving multiple districts and suburbs in and around Rotterdam.
- 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 B Triple: [Florenc, servesMetroLine, Line B]
Generated description
Line B is one of the main lines of the Prague Metro system, running through central Prague and serving key transit hubs and residential districts.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line B Target entity description: Line B is one of the main lines of the Prague Metro system, running through central Prague and serving key transit hubs and residential districts.
-
A.
Line B
chosen
Line B is one of the main lines of the Prague Metro system, running east–west across the city and serving numerous central and residential districts.
-
B.
Line B
Line B is one of the main lines of the Buenos Aires Underground, running through key commercial and residential areas of the city.
-
C.
Line B
Line B is one of the main routes of the Strasbourg tramway network, serving key districts and connecting important transit hubs across the city.
-
D.
Line B
Line B is one of the main routes of the Porto Metro light rail system in Porto, Portugal, connecting key suburban areas with the city center.
-
E.
Line B
Line B is a major Mexico City Metro route that runs diagonally across the city, connecting central areas with northeastern suburbs and serving as an important commuter 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_69d87f198bc48190a8b7e53ca15b7ead |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e1845161908190adca2af94710b2cc |
completed | April 17, 2026, 12:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fff79a96d08190af69cbb18037f66e |
completed | May 10, 2026, 3:12 a.m. |
| NEDg | Description generation | batch_69fff86a556c819096bc008e1ca76e8c |
completed | May 10, 2026, 3:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fff926120081909f1042bf3a16ea10 |
completed | May 10, 2026, 3:19 a.m. |
Created at: April 10, 2026, 4:59 a.m.