Triple
T19823090
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pyynikki Observation Tower |
E476246
|
entity |
| Predicate | caféAccess |
P82097
|
FINISHED |
| Object | free entry |
—
|
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: free entry | Statement: [Pyynikki Observation Tower, caféAccess, free entry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: caféAccess Context triple: [Pyynikki Observation Tower, caféAccess, free entry]
-
A.
hasCafes
Indicates that one entity possesses, contains, or includes one or more cafes within it.
-
B.
coffeeOrganization
Indicates a relationship where an organization is involved with coffee, such as producing, distributing, selling, or promoting it.
-
C.
hasCaffeinatedOption
Indicates that something offers or includes at least one option that contains caffeine.
-
D.
hasLoungeOrCafe
chosen
Indicates that one entity provides or includes access to a lounge or café associated with another entity.
-
E.
coffeeBrand
Indicates that one entity is a brand associated with the production or marketing of coffee products for the other 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_69d8e51c7c188190b926f3a2a7b5f881 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6550070c4819099e1f057b9a8849e |
completed | April 20, 2026, 4:32 p.m. |
| PD | Predicate disambiguation | batch_69e5305bda388190a23b7191768107b1 |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 1:50 p.m.