Triple
T241695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uber |
E4943
|
entity |
| Predicate | hasCompetitor |
P1375
|
FINISHED |
| Object |
Ola Cabs
Ola Cabs is a major Indian ride-hailing company offering app-based transportation and mobility services across numerous cities in India and other countries.
|
E36498
|
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: Ola Cabs | Statement: [Uber, hasCompetitor, Ola Cabs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ola Cabs Context triple: [Uber, hasCompetitor, Ola Cabs]
-
A.
Lyft
Lyft is a major American ride-hailing and transportation company that connects passengers with drivers through a mobile app platform.
-
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.
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.
-
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. 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: Ola Cabs Triple: [Uber, hasCompetitor, Ola Cabs]
Generated description
Ola Cabs is a major Indian ride-hailing company offering app-based transportation and mobility services across numerous cities in India and other countries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ola Cabs Target entity description: Ola Cabs is a major Indian ride-hailing company offering app-based transportation and mobility services across numerous cities in India and other countries.
-
A.
Lyft
Lyft is a major American ride-hailing and transportation company that connects passengers with drivers through a mobile app platform.
-
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.
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.
-
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. 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_69a3949e1e2c819092c702b1fa1cb46c |
completed | March 1, 2026, 1:21 a.m. |
| NEDg | Description generation | batch_69a3986c9b648190a1c30de3b0b52da6 |
completed | March 1, 2026, 1:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a398c6b6588190a4213b66f07471b2 |
completed | March 1, 2026, 1:39 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.