Triple
T11112813
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pérez |
E262802
|
entity |
| Predicate | frequencyInLatinAmerica |
P97328
|
FINISHED |
| Object | very common |
—
|
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: very common | Statement: [Pérez, frequencyInLatinAmerica, very common]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frequencyInLatinAmerica Context triple: [Pérez, frequencyInLatinAmerica, very common]
-
A.
frequencyCategoryInSpain
Indicates the categorized level of how often something occurs or is observed within Spain.
-
B.
frequencyInUS
Indicates how often something occurs, appears, or is used within the United States.
-
C.
frequencyInHungary
Indicates how often something occurs or is present within the context of Hungary.
-
D.
frequencyCategory
Indicates how often an action, event, or relationship occurs, typically by assigning it to a qualitative frequency level (e.g., rare, occasional, frequent).
-
E.
frequencyRegion
Indicates that something is associated with, occurs within, or is characterized by a particular range or band of frequencies.
- F. None of above. chosen
Provenance (4 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_69d6aa9b46cc8190b19f9f0cc45bf322 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79aa523588190a25d241ccc6a9679 |
completed | April 9, 2026, 12:25 p.m. |
| PD | Predicate disambiguation | batch_69d7441cf8188190b8095f622c923156 |
completed | April 9, 2026, 6:15 a.m. |
| PDg | Predicate description generation | batch_69d750ca52ec8190a559432a5de106fd |
completed | April 9, 2026, 7:10 a.m. |
Created at: April 8, 2026, 9:27 p.m.