Triple
T1479272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hollywood Burbank Airport |
E30913
|
entity |
| Predicate | marketingNameEmphasizes |
P29235
|
FINISHED |
| Object | proximity to Hollywood |
—
|
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: proximity to Hollywood | Statement: [Hollywood Burbank Airport, marketingNameEmphasizes, proximity to Hollywood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marketingNameEmphasizes Context triple: [Hollywood Burbank Airport, marketingNameEmphasizes, proximity to Hollywood]
-
A.
marketingNameOf
Indicates that one entity is the marketing or brand name used to promote or refer to another entity.
-
B.
originalMarketingName
Indicates the original marketing or brand name under which an entity (such as a product or service) was first promoted or sold.
-
C.
hasEmphasis
Indicates that one element is given special stress, importance, or prominence relative to others.
-
D.
mottoEmphasizes
Indicates that a motto highlights, stresses, or gives special importance to a particular idea, value, or theme.
-
E.
marketingAs
Indicates that one entity is being presented, promoted, or branded to others as if it were another specified entity, role, or category.
- F. None of above. chosen
Provenance (4 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_69a498fe55a88190ab7f9e40ace88e49 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c6739d2481909ea8d8e075f62cf3 |
completed | March 1, 2026, 11:06 p.m. |
| PD | Predicate disambiguation | batch_69a4c484e52c81908948ff8c0a42751b |
completed | March 1, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69a4c57984088190b2c2d2d9cc2e5df9 |
completed | March 1, 2026, 11:02 p.m. |
Created at: March 1, 2026, 8:11 p.m.