Triple
T28078393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cavanaugh |
E709609
|
entity |
| Predicate | hasTypicalUsageType |
P191568
|
FINISHED |
| Object | family name |
—
|
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: family name | Statement: [Cavanaugh, hasTypicalUsageType, family name]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalUsageType Context triple: [Cavanaugh, hasTypicalUsageType, family name]
-
A.
hasTypicalUseContext
Indicates that something is commonly or characteristically used within a particular situation, setting, or context.
-
B.
hasTypicalUsageRegion
Indicates that something is most commonly or characteristically used within a particular geographic region.
-
C.
hasTypicalAccess
Indicates that one entity normally or customarily has the ability, permission, or means to access or use another entity.
-
D.
hasTypicalUseTime
Indicates the usual or expected duration or time period during which something is commonly used or in operation.
-
E.
hasTypicalCharacterType
Indicates that an entity is commonly associated with or exemplified by a particular type of character or persona.
- 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_69ef9b6f8078819098b741274cd1a2ee |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69fce28d6c3081908bf76f5db63ecf68 |
completed | May 7, 2026, 7:05 p.m. |
| PD | Predicate disambiguation | batch_69fce12d2f08819082134b5eb3db6a24 |
completed | May 7, 2026, 6:59 p.m. |
| PDg | Predicate description generation | batch_69fce28a74508190aab36551094e8226 |
completed | May 7, 2026, 7:05 p.m. |
Created at: April 27, 2026, 8:50 p.m.