Triple
T17087949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Burnett |
E414648
|
entity |
| Predicate | hasFirstNameFrequency |
P19169
|
FINISHED |
| Object | common in English |
—
|
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: common in English | Statement: [John Burnett, hasFirstNameFrequency, common in English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFirstNameFrequency Context triple: [John Burnett, hasFirstNameFrequency, common in English]
-
A.
hasSurnameFrequency
Indicates that a surname occurs with a specified frequency or rate within a given population or dataset.
-
B.
isCommonAsFirstName
chosen
Indicates that the referenced name is frequently used as a first (given) name within a specified population or context.
-
C.
usedAsFirstNameOf
Indicates that one entity functions as the given or first name of another entity.
-
D.
hasFrequencyRankInEngland
Indicates that an entity (such as a name or term) has a specific position in a ranked list based on how frequently it occurs in England.
-
E.
hasNameGenderUsage
Indicates that a particular name is used with a specific gender or set of genders in a given context.
- 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbe92e488190b947287a968086d5 |
completed | April 18, 2026, 7:30 p.m. |
| PD | Predicate disambiguation | batch_69e35d67b14481909fcdbdeaa5c34785 |
completed | April 18, 2026, 10:31 a.m. |
Created at: April 10, 2026, 5:35 a.m.