Triple
T34997973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Porto metropolitan public transport network |
E1009591
|
entity |
| Predicate | metroSystemType |
P114460
|
FINISHED |
| Object | light rail |
—
|
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: light rail | Statement: [Porto metropolitan public transport network, metroSystemType, light rail]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: metroSystemType Context triple: [Porto metropolitan public transport network, metroSystemType, light rail]
-
A.
metroSystemName
Indicates the name assigned to a particular metro (subway) system.
-
B.
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).
-
C.
tramSystemName
Indicates the name assigned to a particular tram system.
-
D.
urbanRailCategory
chosen
Indicates the classification of an urban rail system according to its type or category (e.g., metro, tram, light rail).
-
E.
rapidTransitSystem
Indicates a transportation relationship where people or goods are moved via a high-capacity, high-frequency public transit system designed for rapid travel over urban or regional routes.
- 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_69f78ce78b508190955848e133398dc8 |
completed | May 3, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69f78b8f4cc08190b49fccd798cb25d7 |
completed | May 3, 2026, 5:53 p.m. |
Created at: May 3, 2026, 4:01 p.m.