Triple
T10170022
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abdulla Elyas |
E235305
|
entity |
| Predicate | coFounderOf |
P104
|
FINISHED |
| Object | Careem |
E36562
|
NE FINISHED |
How this triple was built (2 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: Careem | Statement: [Abdulla Elyas, coFounderOf, Careem]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Careem Context triple: [Abdulla Elyas, coFounderOf, Careem]
-
A.
Careem
chosen
Careem is a Dubai-based ride-hailing and delivery company operating across the Middle East, North Africa, and South Asia, acquired by Uber to expand its presence in the region.
-
B.
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.
-
C.
Lyft
Lyft is a major American ride-hailing and transportation company that connects passengers with drivers through a mobile app platform.
-
D.
Uber Pro
Uber Pro is a rewards and loyalty program that provides benefits and incentives to Uber drivers based on their performance and activity.
-
E.
Didi Chuxing
Didi Chuxing is a major Chinese ride-hailing and mobility technology company offering app-based transportation, taxi, and related services across numerous cities in China and abroad.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca84ceafd0819085828600e11bed6b |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdec9d36608190be78665cc3410cf2 |
completed | April 2, 2026, 4:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d300f7aafc8190be874efc755bd188 |
completed | April 6, 2026, 12:40 a.m. |
Created at: March 30, 2026, 9:10 p.m.