Triple
T25621202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waffle House, Inc. |
E642299
|
entity |
| Predicate | hasSecondaryRegionOfLocations |
P131095
|
FINISHED |
| Object | Midwestern 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: Midwestern United States | Statement: [Waffle House, Inc., hasSecondaryRegionOfLocations, Midwestern United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSecondaryRegionOfLocations Context triple: [Waffle House, Inc., hasSecondaryRegionOfLocations, Midwestern United States]
-
A.
hasSecondaryRegion
chosen
Indicates that an entity is associated with an additional, subordinate, or non-primary region beyond its main designated region.
-
B.
hasSecondaryCity
Indicates that an entity possesses or is associated with a secondary city in addition to its primary city.
-
C.
hasMultipleLocalities
Indicates that an entity is associated with more than one locality or geographic area.
-
D.
hasSecondary
Indicates that an entity is associated with an additional or subordinate counterpart beyond its primary one.
-
E.
hasRegion
Indicates that an entity includes, contains, or is associated with a specific geographic or administrative region as part of its scope or structure.
- 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_69e77e7a96748190b10f2699041e4e43 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f6fb19063c81909466b329655c8583 |
completed | May 3, 2026, 7:36 a.m. |
| PD | Predicate disambiguation | batch_69f6f969b4cc8190afb473a2d8b110bc |
completed | May 3, 2026, 7:29 a.m. |
Created at: April 21, 2026, 5:05 p.m.