Triple
T241667
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uber |
E4943
|
entity |
| Predicate | hasBrand |
P1500
|
FINISHED |
| Object |
Uber for Business
Uber for Business is Uber’s platform that helps companies manage and streamline employee transportation and meal programs through centralized tools, controls, and reporting.
|
E4943
|
NE FINISHED |
How this triple was built (4 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 for Business | Statement: [Uber, hasBrand, Uber for Business]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uber for Business Context triple: [Uber, hasBrand, Uber for Business]
-
A.
Uber
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.
UberX
UberX is Uber’s standard, budget-friendly ride option that connects riders with everyday drivers using their personal vehicles.
-
C.
Lyft
Lyft is a major American ride-hailing and transportation company that connects passengers with drivers through a mobile app platform.
-
D.
Uber Black
Uber Black is Uber’s premium ride service offering high-end vehicles and professional drivers for a more luxurious travel experience.
-
E.
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.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Uber for Business Triple: [Uber, hasBrand, Uber for Business]
Generated description
Uber for Business is Uber’s platform that helps companies manage and streamline employee transportation and meal programs through centralized tools, controls, and reporting.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Uber for Business Target entity description: Uber for Business is Uber’s platform that helps companies manage and streamline employee transportation and meal programs through centralized tools, controls, and reporting.
-
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.
UberX
UberX is Uber’s standard, budget-friendly ride option that connects riders with everyday drivers using their personal vehicles.
-
C.
Lyft
Lyft is a major American ride-hailing and transportation company that connects passengers with drivers through a mobile app platform.
-
D.
Uber Black
Uber Black is Uber’s premium ride service offering high-end vehicles and professional drivers for a more luxurious travel experience.
-
E.
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.
- F. None of above.
Provenance (5 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_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25cee6f208190b996be4faa700910 |
completed | Feb. 28, 2026, 3:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a386155c0c8190baa1a1d5df15392e |
completed | March 1, 2026, 12:19 a.m. |
| NEDg | Description generation | batch_69a3867100008190a4d17f099802864c |
completed | March 1, 2026, 12:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a386c0ee288190ae58373c531e6313 |
completed | March 1, 2026, 12:22 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.