Triple
T13339427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catanese |
E317783
|
entity |
| Predicate | typicalCollocation |
P44966
|
FINISHED |
| Object | cucina catanese |
—
|
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: cucina catanese | Statement: [Catanese, typicalCollocation, cucina catanese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCollocation Context triple: [Catanese, typicalCollocation, cucina catanese]
-
A.
commonCollocation
chosen
Indicates that two or more words frequently occur together in natural language usage as a typical or conventional combination.
-
B.
typicalIn
Indicates that something commonly occurs, appears, or is found within a given context, category, or environment.
-
C.
typicalTerm
Indicates that something is a standard, representative, or characteristic term typically associated with a given concept or context.
-
D.
collocatedWith
Indicates that two entities are located in the same place or spatial context at the same time.
-
E.
hasCollocationInformation
Indicates that there is information about how a term or expression typically co-occurs with other words or phrases in usage.
- 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_69d806b5a3c08190b42c267fb092f98a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99d01bf8481908cd3a99e5557b972 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f6e53d88190bd6aa42f69b10ffb |
completed | April 11, 2026, 12:01 a.m. |
Created at: April 9, 2026, 9:31 p.m.