Triple
T6540020
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sector 6 |
E168260
|
entity |
| Predicate | hasPublicTransport |
P1288
|
FINISHED |
| Object | Bucharest Metro |
E160604
|
NE 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: Bucharest Metro | Statement: [Sector 6, hasPublicTransport, Bucharest Metro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bucharest Metro Context triple: [Sector 6, hasPublicTransport, Bucharest Metro]
-
A.
Bucharest Metro
chosen
The Bucharest Metro is the rapid transit system serving Romania’s capital city, providing high-capacity urban rail transport across Bucharest.
-
B.
Sofia Metro
Sofia Metro is the rapid transit system serving Bulgaria’s capital city, providing high-capacity urban rail transport across Sofia and its metropolitan area.
-
C.
Budapest Metro
The Budapest Metro is the rapid transit system serving Hungary’s capital, notable for including Line 1, one of the oldest electrified underground railway lines in continental Europe.
-
D.
Turin Metro
The Turin Metro is a fully automated, driverless rapid transit system serving the city of Turin, Italy.
-
E.
Istanbul Metro
The Istanbul Metro is a rapid transit system serving Istanbul, Turkey, connecting key districts and transport hubs across the city.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c68a51564081909e93aee0dbd9cca3 |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6add5d3848190a0d70dc4013ab756 |
completed | March 27, 2026, 4:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6d53b861c81908adc984a3067d4ef |
completed | March 27, 2026, 7:06 p.m. |
Created at: March 27, 2026, 1:50 p.m.