Triple
T27130727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wharton econometric forecasting model |
E681555
|
entity |
| Predicate | countryModeled |
P6123
|
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: [Wharton econometric forecasting model, countryModeled, United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryModeled Context triple: [Wharton econometric forecasting model, countryModeled, United States]
-
A.
countryIncludes
Indicates that a country geographically or administratively contains or encompasses a specified region, territory, or subdivision.
-
B.
countryScope
chosen
Indicates that something is limited to, applicable within, or defined at the level of a specific country.
-
C.
countrySubject
Indicates that the subject entity is a country that is the main actor, topic, or focus in the described relation or statement.
-
D.
countryCluster
Indicates that multiple countries are grouped together based on shared characteristics, relationships, or classification criteria.
-
E.
regionCountrySide
Indicates that a region is located in or associated with the countryside or rural area of a country.
- 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_69eefacbcc2081909ebf00daa23f1981 |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69fcef654d588190b29ecc76678d1aa0 |
completed | May 7, 2026, 8 p.m. |
| PD | Predicate disambiguation | batch_69fcecdb97f48190b382b7d13be92dc0 |
completed | May 7, 2026, 7:49 p.m. |
Created at: April 27, 2026, 9:04 a.m.