Triple
T241647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uber |
E4943
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object | Uber Technologies, Inc. |
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 Technologies, Inc. | Statement: [Uber, fullName, Uber Technologies, Inc.]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uber Technologies, Inc. Context triple: [Uber, fullName, Uber Technologies, Inc.]
-
A.
Lyft
Lyft is a major American ride-hailing and transportation company that connects passengers with drivers through a mobile app platform.
-
B.
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.
-
C.
Byfleet
Byfleet is a village and former civil parish in southeast England, situated within the county of Surrey.
-
D.
The Boring Company
The Boring Company is an infrastructure and tunnel construction firm founded by Elon Musk to develop underground transportation systems aimed at reducing urban traffic congestion.
-
E.
Snap Inc.
Snap Inc. is an American technology and social media company best known for developing the multimedia messaging app Snapchat and related camera and augmented reality products.
- 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.