Triple
T16094001
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brickell Avenue |
E390432
|
entity |
| Predicate | nearbyContains |
P118122
|
FINISHED |
| Object | luxury hotels |
—
|
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: luxury hotels | Statement: [Brickell Avenue, nearbyContains, luxury hotels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyContains Context triple: [Brickell Avenue, nearbyContains, luxury hotels]
-
A.
nearbyTo
Indicates that one entity is located close in distance or position to another entity.
-
B.
hasNearbyPoint
chosen
Indicates that one entity has at least one other point located within a specified proximity or distance from it.
-
C.
hasNearbyFunction
Indicates that one entity has another entity located close by that serves a related or supportive function.
-
D.
meetsNear
Indicates that two entities meet or come together at a location that is in close proximity to a specified reference point or area.
-
E.
hasNearbyCommon
Indicates that two entities share at least one common element, feature, or connection that is located within a specified nearby distance or vicinity.
- 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_69d87f198bc48190a8b7e53ca15b7ead |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e1ff63edb0819092cbb671967bbdcd |
completed | April 17, 2026, 9:37 a.m. |
| PD | Predicate disambiguation | batch_69e182804208819087f35307cd6e4103 |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 4:59 a.m.