Triple
T13215972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nice (Breeze Airways cabin) |
E314616
|
entity |
| Predicate | relativePositionInBrand |
P48676
|
FINISHED |
| Object | base fare product |
—
|
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: base fare product | Statement: [Nice (Breeze Airways cabin), relativePositionInBrand, base fare product]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relativePositionInBrand Context triple: [Nice (Breeze Airways cabin), relativePositionInBrand, base fare product]
-
A.
positionInBrandPortfolio
chosen
Indicates the relative role or rank that a brand holds within a company’s overall brand portfolio.
-
B.
relativePositioning
Indicates how one entity is spatially arranged or located in relation to another entity.
-
C.
relativePosition
Indicates the spatial relationship of one entity’s location with respect to another entity’s position.
-
D.
brandPositioning
Indicates how a brand is strategically placed and perceived in the minds of its target audience relative to competitors.
-
E.
positionedRelativeTo
Indicates that one entity’s spatial location is defined or described in relation to the position of another entity.
- 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_69d806aee7308190b70a237ba2a6e3e1 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98cf28c9c819080d7b42d20f579d1 |
completed | April 10, 2026, 11:51 p.m. |
| PD | Predicate disambiguation | batch_69d98bc938f081909f123bdf1263ff7f |
completed | April 10, 2026, 11:46 p.m. |
Created at: April 9, 2026, 9:18 p.m.