Triple
T35674965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emmalee |
E1030831
|
entity |
| Predicate | hasNameUsageFrequency |
P150893
|
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: [Emmalee, hasNameUsageFrequency, uncommon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNameUsageFrequency Context triple: [Emmalee, hasNameUsageFrequency, uncommon]
-
A.
hasGivenNameFrequency
chosen
Indicates the frequency or commonness with which a particular given name occurs within a specified population or context.
-
B.
hasSurnameFrequency
Indicates that a surname occurs with a specified frequency or rate within a given population or dataset.
-
C.
hasNameGenderUsage
Indicates that a particular name is used with a specific gender or set of genders in a given context.
-
D.
nameUsageStatus
Indicates the current status or condition of how a name is being used or applied in a given context.
-
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_69f76e0acfc0819082c8495c2210ce73 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ff4de66ba481908e7184b3cf9d4d2d |
completed | May 9, 2026, 3:08 p.m. |
| PD | Predicate disambiguation | batch_69ff4c702a5881909c6684c74807e945 |
completed | May 9, 2026, 3:02 p.m. |
Created at: May 3, 2026, 4:05 p.m.