Triple
T3506662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rose Garden |
E74091
|
entity |
| Predicate | hasConcessionStands |
P17433
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Rose Garden, hasConcessionStands, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasConcessionStands Context triple: [Rose Garden, hasConcessionStands, yes]
-
A.
hasConcessions
chosen
Indicates that one entity provides or contains concession facilities, services, or rights (such as food, drink, or merchandise sales) for another entity or within a given context.
-
B.
hasGrandstandFeature
Indicates that something possesses or includes a grandstand-related feature or characteristic.
-
C.
concessionType
Indicates the specific kind or category of concession (such as a discount, exemption, or special allowance) that applies in a given context.
-
D.
hasEntertainmentVenue
Indicates that an entity possesses, contains, or is associated with an entertainment venue as part of its facilities or offerings.
-
E.
concessionExtended
Indicates that one party has granted or prolonged a special allowance, discount, or favorable term to another party.
- 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbbf52bd8819085a2ac5f48cc5c68 |
completed | March 8, 2026, 6:12 p.m. |
| PD | Predicate disambiguation | batch_69adae0e770481908528fa35eda53003 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:18 p.m.