Triple
T241668
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uber |
E4943
|
entity |
| Predicate | hasBrand |
P1500
|
FINISHED |
| Object | Uber Reserve |
E4943
|
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: Uber Reserve | Statement: [Uber, hasBrand, Uber Reserve]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uber Reserve Context triple: [Uber, hasBrand, Uber Reserve]
-
A.
Airbnb
Airbnb is a global online marketplace that connects people seeking short-term lodging or experiences with hosts offering accommodations and activities in locations around the world.
-
B.
Lyft
Lyft is a major American ride-hailing and transportation company that connects passengers with drivers through a mobile app platform.
-
C.
Uber
chosen
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.
-
D.
Byfleet
Byfleet is a village and former civil parish in southeast England, situated within the county of Surrey.
-
E.
Ventra
Ventra is the contactless fare payment system used across Chicago’s public transit network, including buses and trains.
- 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_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25cee6f208190b996be4faa700910 |
completed | Feb. 28, 2026, 3:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a36961e5688190b3a1ff61bb06233c |
completed | Feb. 28, 2026, 10:17 p.m. |
Created at: Feb. 28, 2026, 2:53 a.m.