Triple
T1688612
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ola Cabs |
E36498
|
entity |
| Predicate | brandName |
P1500
|
FINISHED |
| Object |
Ola
Ola is a major Indian ride-hailing and mobility platform offering cab, auto-rickshaw, and other transportation services via its mobile app.
|
E197052
|
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 | Statement: [Ola Cabs, brandName, Ola]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ola Context triple: [Ola Cabs, brandName, Ola]
-
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.
Ola Share
Ola Share is a ride-sharing service by Ola that allows multiple passengers heading in the same direction to share a cab and split the fare.
-
D.
OLIN
OLIN is a prominent landscape architecture and urban design firm known for shaping major public spaces and environmentally responsive projects worldwide.
-
E.
Essa
Essa is a rural township in Simcoe County, Ontario, Canada, known for its agricultural landscape and proximity to the city of Barrie.
- 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 Triple: [Ola Cabs, brandName, Ola]
Generated description
Ola is a major Indian ride-hailing and mobility platform offering cab, auto-rickshaw, and other transportation services via its mobile app.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ola Target entity description: Ola is a major Indian ride-hailing and mobility platform offering cab, auto-rickshaw, and other transportation services via its 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.
Ola Share
Ola Share is a ride-sharing service by Ola that allows multiple passengers heading in the same direction to share a cab and split the fare.
-
D.
OLIN
OLIN is a prominent landscape architecture and urban design firm known for shaping major public spaces and environmentally responsive projects worldwide.
-
E.
Essa
Essa is a rural township in Simcoe County, Ontario, Canada, known for its agricultural landscape and proximity to the city of Barrie.
- 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.