Triple
T2214165
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Durant |
E50984
|
entity |
| Predicate | hasFrequencyRankInUnitedStates |
P29279
|
FINISHED |
| Object | relatively common |
—
|
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: relatively common | Statement: [Durant, hasFrequencyRankInUnitedStates, relatively common]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFrequencyRankInUnitedStates Context triple: [Durant, hasFrequencyRankInUnitedStates, relatively common]
-
A.
frequencyRankInUnitedStates
chosen
Indicates the relative position of something in an ordered list based on how frequently it occurs within the United States.
-
B.
areaRankInUS
Indicates the relative position of an entity in a ranking of areas within the United States, based on its size.
-
C.
frequencyInUS
Indicates how often something occurs, appears, or is used within the United States.
-
D.
countryRankContext
Indicates the relative position or ranking of a country within a specified contextual framework (such as economic, political, or performance-based criteria).
-
E.
rankByPopulationInUnitedStates
Indicates the relative ordering of entities based on their population size within the United States.
- 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_69a88b06709c8190978fb2418470d1b6 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abbfef875c8190b642736b4cc11d4c |
completed | March 7, 2026, 6:04 a.m. |
| PD | Predicate disambiguation | batch_69abbdaa26d48190860c33fd464c4845 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:46 p.m.