Triple
T11417036
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Townsend |
E270517
|
entity |
| Predicate | hasFrequencyCategoryInUnitedStates |
P28499
|
FINISHED |
| Object | common surname |
—
|
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 surname | Statement: [Townsend, hasFrequencyCategoryInUnitedStates, common surname]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFrequencyCategoryInUnitedStates Context triple: [Townsend, hasFrequencyCategoryInUnitedStates, common surname]
-
A.
hasFrequencyCategory
chosen
Indicates that something is associated with a particular classification of how often it occurs or is used.
-
B.
frequencyInUS
Indicates how often something occurs, appears, or is used within the United States.
-
C.
hasFrequencyCoverage
Indicates that one entity provides, supports, or is applicable across a specified range or set of frequencies associated with another entity.
-
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_69d6aaddeaa8819088b30ef7b50598c9 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d801b0236c81908122ce3fc7b4fde7 |
completed | April 9, 2026, 7:44 p.m. |
| PD | Predicate disambiguation | batch_69d7e70ffd708190b62a78ebcbce9f78 |
completed | April 9, 2026, 5:51 p.m. |
Created at: April 8, 2026, 9:34 p.m.