Triple
T33626261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Budapest transport network |
E861412
|
entity |
| Predicate | metroSystemName |
P176971
|
FINISHED |
| Object | Budapest Metro |
—
|
NE NERFINISHED |
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: Budapest Metro | Statement: [Budapest transport network, metroSystemName, Budapest Metro]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: metroSystemName Context triple: [Budapest transport network, metroSystemName, Budapest Metro]
-
A.
tramSystemName
Indicates the name assigned to a particular tram system.
-
B.
lightRailSystemName
Indicates the name assigned to a particular light rail transit system.
-
C.
subwayLine
Indicates that there is a subway line connection or service relationship between the referenced entities.
-
D.
metro
Indicates a relationship where an entity is associated with, located in, or served by a metropolitan transit system (such as a subway or urban rail network).
-
E.
metroSystemColor
Indicates the color assigned to a metro or subway line within a transit system.
- F. None of above. chosen
Provenance (4 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_69f34981c54c81909b33c3fa2208a52d |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f85724048190be13f0503898a67e |
completed | May 3, 2026, 7:25 a.m. |
| PD | Predicate disambiguation | batch_69f6f6632dfc8190af85e258c8519207 |
completed | May 3, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69f6f70b0ca081908b24a98937e6ef66 |
completed | May 3, 2026, 7:19 a.m. |
Created at: May 1, 2026, 1:41 a.m.