Triple
T29897832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huffington |
E759324
|
entity |
| Predicate | countryOfNotableUse |
P135380
|
FINISHED |
| Object | United States |
—
|
NE NERFINISHED |
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: United States | Statement: [Huffington, countryOfNotableUse, United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryOfNotableUse Context triple: [Huffington, countryOfNotableUse, United States]
-
A.
countryOfSymbolicUse
Indicates the country in which something (such as a symbol, object, or element) is used in a symbolic or representative way.
-
B.
usedByCountriesWith
Indicates that something (such as an item, system, or practice) is utilized or employed by one or more specified countries in common.
-
C.
countryNameUsage
Indicates how a country’s name is used or applied in a particular context or representation.
-
D.
countryOfNotableSuccess
Indicates the country in which an entity achieved notable success or recognition.
-
E.
primaryUseCountry
chosen
Indicates the country in which something is primarily used or most commonly utilized.
- 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_69f2245f1cf88190978c70d1a1d2cb73 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69ff409ff5548190849c2d50e99bd807 |
completed | May 9, 2026, 2:11 p.m. |
| PD | Predicate disambiguation | batch_69ff401a5e188190a72f945e910b4a6c |
completed | May 9, 2026, 2:09 p.m. |
Created at: April 29, 2026, 6:05 p.m.