Triple
T34997978
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Porto metropolitan public transport network |
E1009591
|
entity |
| Predicate | metroLinesCount |
P155465
|
FINISHED |
| Object | 6 |
—
|
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: 6 | Statement: [Porto metropolitan public transport network, metroLinesCount, 6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: metroLinesCount Context triple: [Porto metropolitan public transport network, metroLinesCount, 6]
-
A.
numberOfMetroLines
chosen
Indicates the total count of metro (subway) lines associated with or serving a given entity.
-
B.
line1NumberOfStations
Indicates the total count of stations associated with or located along line 1.
-
C.
subwayLine
Indicates that there is a subway line connection or service relationship between the referenced entities.
-
D.
metroLineColor
Indicates the color assigned to a specific metro or subway line in a transit system.
-
E.
metroLineInaugurated
Indicates that a metro line was officially opened and began operation on a specific date or during a specific event.
- 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_69f76dca50dc8190b71f39defe186be8 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f79f48acec8190a9d5964581a94f6c |
completed | May 3, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69f79e4888248190be2f63cdfb5cd7b7 |
completed | May 3, 2026, 7:13 p.m. |
Created at: May 3, 2026, 4:01 p.m.