Triple
T37410456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arbeyal Beach |
E929550
|
entity |
| Predicate | hasNearbyBarsAndRestaurants |
P101754
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Arbeyal Beach, hasNearbyBarsAndRestaurants, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyBarsAndRestaurants Context triple: [Arbeyal Beach, hasNearbyBarsAndRestaurants, true]
-
A.
hasNumberOfRestaurantsAndBars
Indicates the total count of restaurants and bars associated with a given entity.
-
B.
hasRestaurantsAndBars
chosen
Indicates that the subject location contains or provides access to both restaurants and bars.
-
C.
nearbyVenue
Indicates that one venue is located close to another venue in physical space.
-
D.
hasBeachBarsNearby
Indicates that there are beach bars located in close proximity to the referenced place or area.
-
E.
hasNearbyCommercialFacilities
Indicates that a place is located close to one or more commercial facilities, such as shops, restaurants, or other businesses.
- 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_69f76ebde49481908566cd96b37ccc84 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a009d623f6c8190b702e2892c52fbb2 |
completed | May 10, 2026, 2:59 p.m. |
| PD | Predicate disambiguation | batch_6a009a3050d48190b64567f28e6ea463 |
completed | May 10, 2026, 2:46 p.m. |
Created at: May 3, 2026, 4:16 p.m.