Triple
T29214488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eleventh United States decennial census |
E740627
|
entity |
| Predicate | legalFrequency |
P8512
|
FINISHED |
| Object | every 10 years |
—
|
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: every 10 years | Statement: [Eleventh United States decennial census, legalFrequency, every 10 years]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalFrequency Context triple: [Eleventh United States decennial census, legalFrequency, every 10 years]
-
A.
frequencyRequirement
chosen
Indicates a constraint specifying how often an action, event, or condition must occur within a given context or time frame.
-
B.
usesFrequency
Indicates that one entity employs or operates another entity at a specified rate, interval, or number of occurrences over time.
-
C.
constitutionalFrequency
Indicates how often a constitutional or fundamental condition, symptom, or state occurs over a given period.
-
D.
frequencyStandard
Indicates that one entity serves as the reference or baseline frequency against which another entity’s frequency is defined, measured, or calibrated.
-
E.
usualFrequency
Indicates how often an action, event, or relationship typically occurs within a given time frame.
- 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_69f07cba2f808190a2746477d4e8345b |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69fd68abf52881909c5a390c362b7c59 |
completed | May 8, 2026, 4:38 a.m. |
| PD | Predicate disambiguation | batch_69fd6812d0c88190930d8fa2d4b92490 |
completed | May 8, 2026, 4:35 a.m. |
Created at: April 28, 2026, 12:12 p.m.