Triple
T241669
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uber |
E4943
|
entity |
| Predicate | hasBrand |
P1500
|
FINISHED |
| Object |
Uber Green
Uber Green is an eco-focused ride option from Uber that connects riders with drivers using low-emission or electric vehicles.
|
E34713
|
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 Green | Statement: [Uber, hasBrand, Uber Green]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uber Green Context triple: [Uber, hasBrand, Uber Green]
-
A.
Uber Black
Uber Black is Uber’s premium ride service offering high-end vehicles and professional drivers for a more luxurious travel experience.
-
B.
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.
-
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.
UberXL
UberXL is a ride option from Uber that provides larger vehicles suitable for groups or extra luggage, typically at a higher fare than standard rides.
- 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 Green Triple: [Uber, hasBrand, Uber Green]
Generated description
Uber Green is an eco-focused ride option from Uber that connects riders with drivers using low-emission or electric vehicles.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Uber Green Target entity description: Uber Green is an eco-focused ride option from Uber that connects riders with drivers using low-emission or electric vehicles.
-
A.
Uber Black
Uber Black is Uber’s premium ride service offering high-end vehicles and professional drivers for a more luxurious travel experience.
-
B.
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.
-
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.
UberXL
UberXL is a ride option from Uber that provides larger vehicles suitable for groups or extra luggage, typically at a higher fare than standard rides.
- F. None of above. chosen
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_69a389a9bb7c81909b60569e4c6074fb |
completed | March 1, 2026, 12:34 a.m. |
| NEDg | Description generation | batch_69a38a53a07881908a1e1a0773680044 |
completed | March 1, 2026, 12:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a38acac1ec81909578321bb8b0bfd2 |
completed | March 1, 2026, 12:39 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.