Triple
T32512737
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mister Donut |
E830979
|
entity |
| Predicate | originalHeadquartersCountry |
P59352
|
FINISHED |
| Object | United States |
E14
|
NE 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: United States | Statement: [Mister Donut, originalHeadquartersCountry, United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalHeadquartersCountry Context triple: [Mister Donut, originalHeadquartersCountry, United States]
-
A.
formerHeadquartersCountry
Indicates the country in which an entity’s headquarters used to be located before being moved or closed.
-
B.
parentCountryOfOrigin
Indicates that one country is the original source or homeland from which another country historically emerged or originated.
-
C.
formerCountryOfOrigin
Indicates that an entity was previously the country from which another entity originated, but is no longer its current country of origin.
-
D.
originalLocationCountry
chosen
Indicates the country where an entity was originally located or from which it initially originated.
-
E.
historicalOriginCountry
Indicates the country from which something originally came or first emerged in a historical context.
- F. None of above.
Provenance (4 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_69f3492318348190ba37fb6b5f1d67f4 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a01b241f7308190bedb7522cb5dc069 |
completed | May 11, 2026, 10:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a34703daedc8190a57e9225fbdd1ec4 |
completed | June 18, 2026, 10:25 p.m. |
| PD | Predicate disambiguation | batch_6a01b19ff12c81908ee1b188b4a3e118 |
completed | May 11, 2026, 10:38 a.m. |
Created at: May 1, 2026, 1 a.m.