Triple
T21615071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valerie Jarrett |
E533415
|
entity |
| Predicate | boardMemberOf |
P10
|
FINISHED |
| Object | Lyft |
—
|
NE NERFINISHED |
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: Lyft | Statement: [Valerie Jarrett, boardMemberOf, Lyft]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lyft Context triple: [Valerie Jarrett, boardMemberOf, Lyft]
-
A.
Lyft
chosen
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.
Uber Pro
Uber Pro is a rewards and loyalty program that provides benefits and incentives to Uber drivers based on their performance and activity.
-
D.
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.
-
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 (2 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_69e0c46411108190bba0d4176dffc9f3 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef3ba9aca48190b5180eefd61a9fdc |
completed | April 27, 2026, 10:34 a.m. |
Created at: April 16, 2026, 6:33 p.m.