Triple
T1549310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dara Khosrowshahi |
E33050
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Uber |
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 | Statement: [Dara Khosrowshahi, employer, Uber]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uber Context triple: [Dara Khosrowshahi, employer, Uber]
-
A.
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.
-
B.
Uber Pro
Uber Pro is a rewards and loyalty program that provides benefits and incentives to Uber drivers based on their performance and activity.
-
C.
UberX
UberX is Uber’s standard, budget-friendly ride option that connects riders with everyday drivers using their personal vehicles.
-
D.
Lyft
Lyft is a major American ride-hailing and transportation company that connects passengers with drivers through a mobile app platform.
-
E.
Uber Black
Uber Black is Uber’s premium ride service offering high-end vehicles and professional drivers for a more luxurious travel experience.
- 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_69a885ee6db8819099502bc5ce8af881 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90857bfb48190a2d66a601d228b72 |
completed | March 5, 2026, 4:36 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad58b7e1088190af92bc0cc8fddfbe |
completed | March 8, 2026, 11:08 a.m. |
Created at: March 4, 2026, 7:26 p.m.