Triple
T31107244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rowley |
E792826
|
entity |
| Predicate | hasFrequencyCategoryInUnitedKingdom |
P28499
|
FINISHED |
| Object | uncommon |
—
|
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: uncommon | Statement: [Rowley, hasFrequencyCategoryInUnitedKingdom, uncommon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFrequencyCategoryInUnitedKingdom Context triple: [Rowley, hasFrequencyCategoryInUnitedKingdom, uncommon]
-
A.
hasFrequencyCategory
chosen
Indicates that something is associated with a particular classification of how often it occurs or is used.
-
B.
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.
-
C.
hasApproximateEuropeanFrequency
Indicates that the subject has an estimated or approximate occurrence rate within European populations.
-
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.
usesFrequency
Indicates that one entity employs or operates another entity at a specified rate, interval, or number of occurrences over time.
- 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_69f224cfd5d881908ec6447bc321cd58 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f70e8755a48190931eaa77946f9460 |
completed | May 3, 2026, 8:59 a.m. |
| PD | Predicate disambiguation | batch_69f70abc00848190a1c3f495ef6c8dc6 |
completed | May 3, 2026, 8:43 a.m. |
Created at: April 29, 2026, 9:03 p.m.