Triple
T633950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SoftBank Vision Fund |
E15980
|
entity |
| Predicate | investsIn |
P17330
|
FINISHED |
| Object | Didi Chuxing |
E79652
|
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: Didi Chuxing | Statement: [SoftBank Vision Fund, investsIn, Didi Chuxing]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Didi Chuxing Context triple: [SoftBank Vision Fund, investsIn, Didi Chuxing]
-
A.
Didi Chuxing
chosen
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.
-
B.
Careem
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.
-
C.
Lyft
Lyft is a major American ride-hailing and transportation company that connects passengers with drivers through a mobile app platform.
-
D.
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.
-
E.
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.
- 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_69a4935c131c8190a5378c6bf101e8cc |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a515ceb081908c064b2082047c0f |
completed | March 1, 2026, 8:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a57405d6f48190b55542d50d3a1f22 |
completed | March 2, 2026, 11:27 a.m. |
Created at: March 1, 2026, 7:35 p.m.