Triple
T14888374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Budapest Metro Line 3 |
E359687
|
entity |
| Predicate | isBusiestLine |
P26423
|
FINISHED |
| Object | one of the busiest lines of Budapest Metro |
—
|
LITERAL FINISHED |
How this triple was built (2 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: one of the busiest lines of Budapest Metro | Statement: [Budapest Metro Line 3, isBusiestLine, one of the busiest lines of Budapest Metro]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isBusiestLine Context triple: [Budapest Metro Line 3, isBusiestLine, one of the busiest lines of Budapest Metro]
-
A.
isBusiestInSystem
chosen
Indicates that an entity has the highest level of activity or load compared to all other entities within the same system.
-
B.
queueLength
Indicates the current number of items or entities waiting in a queue.
-
C.
isBusiestStationIn
Indicates that a station has the highest level of activity (e.g., passenger or traffic volume) within a specified area or system.
-
D.
frontLineLength
Indicates the total measured extent of the front line where opposing forces or boundaries directly face each other.
-
E.
isBusiestTerminalOf
Indicates that one terminal is the busiest (i.e., handles the highest volume of activity) among all terminals associated with a given entity, such as an airport or transportation hub.
- F. None of above.
Provenance (3 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_69d827980cbc8190a0c569ae3940a1d9 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69ded5f6cf5c8190b6b28f58fafe5d59 |
completed | April 15, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69de8c1a2bcc81908f914e2e2ced65eb |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 2:08 a.m.