Triple
T796811
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dubai Metro |
E17040
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | Dubai Metro |
E17040
|
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: Dubai Metro | Statement: [Dubai Metro, shortName, Dubai Metro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dubai Metro Context triple: [Dubai Metro, shortName, Dubai Metro]
-
A.
Dubai Metro
chosen
The Dubai Metro is a fully automated, driverless urban rail network that serves as a major public transportation backbone across key areas of Dubai.
-
B.
Dubai Tram
Dubai Tram is a modern light rail transit system serving key residential and commercial areas along Dubai’s Al Sufouh and Marina districts.
-
C.
Tehran Metro
Tehran Metro is the rapid transit system serving Iran’s capital, providing extensive urban and suburban rail transport across the Tehran metropolitan area.
-
D.
Dubai Bus
Dubai Bus is the city’s public bus network, providing extensive, scheduled urban and intercity transportation across Dubai and surrounding areas.
-
E.
Tunis Metro
The Tunis Metro is a light rail transit system serving the city of Tunis, providing urban and suburban passenger transport across the Tunisian capital.
- 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_69a49378b9c48190adbf5f62e5b7aca1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a7b172e88190a26d31c9075b81fb |
completed | March 1, 2026, 8:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a67effd3b481909036bdc43d7b909f |
completed | March 3, 2026, 6:26 a.m. |
Created at: March 1, 2026, 7:38 p.m.