Triple
T1688605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ola Cabs |
E36498
|
entity |
| Predicate | offersProduct |
P10882
|
FINISHED |
| Object |
Ola Auto
Ola Auto is a ride-hailing service segment of Ola Cabs that connects passengers with auto-rickshaw drivers via the Ola mobile app.
|
E197051
|
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 Auto | Statement: [Ola Cabs, offersProduct, Ola Auto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ola Auto Context triple: [Ola Cabs, offersProduct, Ola Auto]
-
A.
Ola Outstation
Ola Outstation is a long-distance ride service from Ola Cabs that lets users book intercity and out-of-town trips via the Ola app.
-
B.
Ola Micro
Ola Micro is a budget-friendly ride option from Indian ride-hailing company Ola, offering low-cost cab services for short-distance urban travel.
-
C.
Volvo Cars
Volvo Cars is a Swedish automotive manufacturer known for its focus on safety, practical design, and premium vehicles.
-
D.
Mobil
Mobil is a major American oil company and fuel brand that became part of ExxonMobil after a 1999 merger.
-
E.
Saab Automobile
Saab Automobile was a Swedish car manufacturer known for its innovative engineering, turbocharged engines, and distinctive, safety-focused designs.
- 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 Auto Triple: [Ola Cabs, offersProduct, Ola Auto]
Generated description
Ola Auto is a ride-hailing service segment of Ola Cabs that connects passengers with auto-rickshaw drivers via the Ola mobile app.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ola Auto Target entity description: Ola Auto is a ride-hailing service segment of Ola Cabs that connects passengers with auto-rickshaw drivers via the Ola mobile app.
-
A.
Ola Outstation
Ola Outstation is a long-distance ride service from Ola Cabs that lets users book intercity and out-of-town trips via the Ola app.
-
B.
Ola Micro
Ola Micro is a budget-friendly ride option from Indian ride-hailing company Ola, offering low-cost cab services for short-distance urban travel.
-
C.
Volvo Cars
Volvo Cars is a Swedish automotive manufacturer known for its focus on safety, practical design, and premium vehicles.
-
D.
Mobil
Mobil is a major American oil company and fuel brand that became part of ExxonMobil after a 1999 merger.
-
E.
Saab Automobile
Saab Automobile was a Swedish car manufacturer known for its innovative engineering, turbocharged engines, and distinctive, safety-focused designs.
- 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_69a886151508819084fa7f1ce6e05577 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa6296655c8190835ec0d20f7460ca |
completed | March 6, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada0cb18348190baf7a30c231c7349 |
completed | March 8, 2026, 4:16 p.m. |
| NEDg | Description generation | batch_69ada28b008c8190a46570a9f5f9f7a3 |
completed | March 8, 2026, 4:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ada4dfc9188190845a4e4490318d68 |
completed | March 8, 2026, 4:33 p.m. |
Created at: March 4, 2026, 7:29 p.m.