Triple
T1615748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uber Green |
E34713
|
entity |
| Predicate | availableVia |
P9675
|
FINISHED |
| Object | Uber mobile application |
E4943
|
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: Uber mobile application | Statement: [Uber Green, availableVia, Uber mobile application]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uber mobile application Context triple: [Uber Green, availableVia, Uber mobile application]
-
A.
Uber Pro
Uber Pro is a rewards and loyalty program that provides benefits and incentives to Uber drivers based on their performance and activity.
-
B.
Uber Pool
Uber Pool is a ride-sharing service from Uber that matches multiple passengers heading in similar directions to share a car and split the fare.
-
C.
Careem
Careem is a Dubai-based ride-hailing and delivery company operating across the Middle East, North Africa, and South Asia, acquired by Uber to expand its presence in the region.
-
D.
Uber
chosen
Uber is a global ride-hailing and technology company that connects passengers with drivers through a mobile app and has expanded into food delivery and freight services.
-
E.
UberX
UberX is Uber’s standard, budget-friendly ride option that connects riders with everyday drivers using their personal vehicles.
- 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_69a885ffc5ec819091afa325d5f9611c |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9099049e0819099763ecb09fb4f57 |
completed | March 5, 2026, 4:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad58c6d7e88190b9fc0e34a007a2f5 |
completed | March 8, 2026, 11:08 a.m. |
Created at: March 4, 2026, 7:28 p.m.