Triple
T241665
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uber |
E4943
|
entity |
| Predicate | hasBrand |
P1500
|
FINISHED |
| Object | Uber Eats |
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 Eats | Statement: [Uber, hasBrand, Uber Eats]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uber Eats Context triple: [Uber, hasBrand, Uber Eats]
-
A.
IND Subway
IND Subway is the city-owned Independent Subway System in New York City, built in the early 20th century to compete with private transit operators and now forming a core part of the modern NYC Subway.
-
B.
Lyft
Lyft is a major American ride-hailing and transportation company that connects passengers with drivers through a mobile app platform.
-
C.
Pizza Hut
Pizza Hut is a global American restaurant chain known for its pizza, pasta, and other Italian-American dishes, operating thousands of locations worldwide.
-
D.
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.
-
E.
360 Restaurant
360 Restaurant is a revolving fine-dining restaurant located atop Toronto’s CN Tower, offering panoramic city and lake views.
- 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.