Triple
T10309554
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uber Blue |
E241850
|
entity |
| Predicate | tierRank |
P37057
|
FINISHED |
| Object | lowest tier in Uber Pro |
—
|
LITERAL 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: lowest tier in Uber Pro | Statement: [Uber Blue, tierRank, lowest tier in Uber Pro]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tierRank Context triple: [Uber Blue, tierRank, lowest tier in Uber Pro]
-
A.
rankTier
chosen
Indicates the classification level or tier assigned to an entity within a ranking or hierarchical system.
-
B.
depthRank
Indicates the relative ordering of entities based on how deep or distant they are along a specified depth dimension or hierarchy.
-
C.
rankingScope
Indicates the context or domain within which a ranking is defined, interpreted, or applied.
-
D.
capacityRank
Indicates the relative ordering of entities based on how much capacity (e.g., volume, throughput, or capability) they possess compared to others.
-
E.
rankingType
Indicates the specific basis or method by which items are ordered or ranked relative to one another.
- F. None of above.
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_69d381ac38808190a8ca7457c85b625b |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d7ccb7ec8190a538cf279e48116e |
completed | April 7, 2026, 10:09 a.m. |
| PD | Predicate disambiguation | batch_69d4d1f4f354819080b4ed4bc61bdff6 |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:47 a.m.