Triple
T19454948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scoops Ahoy |
E486710
|
entity |
| Predicate | hasCounterArea |
P88384
|
FINISHED |
| Object | ice cream serving counter |
—
|
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: ice cream serving counter | Statement: [Scoops Ahoy, hasCounterArea, ice cream serving counter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCounterArea Context triple: [Scoops Ahoy, hasCounterArea, ice cream serving counter]
-
A.
hasAreaNumber
Indicates that an entity is associated with a specific area identified by a numerical code.
-
B.
hasCounterSubject
Indicates that a subject is associated with another subject that serves as its counterpart, opposite, or contrasting entity in a given context.
-
C.
hasAreaTotal
Indicates the total surface area associated with an entity, typically measured over its entire extent.
-
D.
hasMacroArea
Indicates that one entity belongs to, or is located within, a broader geographic or conceptual macro-area represented by another entity.
-
E.
hasCounterService
chosen
Indicates that a place provides service to customers over a counter, such as ordering, paying, or receiving items at a service counter.
- 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633c2b1108190b492ca23487b91f8 |
completed | April 20, 2026, 2:10 p.m. |
| PD | Predicate disambiguation | batch_69e4fd7499a4819082bec0be8afba35c |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:38 p.m.