Triple
T4118435
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Von Maur |
E90349
|
entity |
| Predicate | hasRetailOutletIn |
P26597
|
FINISHED |
| Object | Indiana |
E32567
|
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: Indiana | Statement: [Von Maur, hasRetailOutletIn, Indiana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Indiana Context triple: [Von Maur, hasRetailOutletIn, Indiana]
-
A.
Indiana
chosen
Indiana is a U.S. state known for its manufacturing base, rich agricultural land, and iconic events like the Indianapolis 500.
-
B.
Indiana and Kentucky
Indiana and Kentucky are neighboring U.S. states in the Midwest and Upper South, respectively, separated in large part by the Ohio River.
-
C.
Illinois
Illinois is a Midwestern U.S. state known for its major metropolis Chicago, diverse economy, and significant political and transportation influence.
-
D.
Ohio
Ohio is a Midwestern U.S. state known for its diverse economy, major cities like Columbus, Cleveland, and Cincinnati, and its significant role in national politics as a historic swing state.
-
E.
Michigan
Michigan is a U.S. state in the Great Lakes region known for its extensive freshwater coastline, automotive industry centered in Detroit, and diverse natural landscapes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69aed95c080881908125e30c5dcdc6f8 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af0b2c76fc8190b3cd9facfcd6e427 |
completed | March 9, 2026, 6:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5d04bc33c819082cf79f4445610f1 |
completed | March 14, 2026, 9:16 p.m. |
Created at: March 9, 2026, 3:41 p.m.