Triple
T17121875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elder-Beerman |
E415487
|
entity |
| Predicate | headquartersLocation |
P62
|
FINISHED |
| Object | Dayton, Ohio |
E1190566
|
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: Dayton, Ohio | Statement: [Elder-Beerman, headquartersLocation, Dayton, Ohio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dayton, Ohio Context triple: [Elder-Beerman, headquartersLocation, Dayton, Ohio]
-
A.
Dayton, Ohio
chosen
Dayton, Ohio is a mid-sized city in southwestern Ohio known as a historic center of aviation innovation and manufacturing.
-
B.
Dayton
Dayton is a small town located in the state of Indiana in the United States.
-
C.
Dayton
Dayton is an unincorporated community and census-designated place located within South Brunswick Township in Middlesex County, New Jersey.
-
D.
Dayton
Dayton is a mid-sized city in southwestern Ohio known for its historic role in aviation, manufacturing, and research, including its close association with major U.S. Air Force installations.
-
E.
Dayton
Dayton is a masculine given name of English origin used both as a first name and a surname.
- 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_69d886d090cc8190a39cb94992586905 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3e80ac2cc819084fab829917c950a |
completed | April 18, 2026, 8:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a014147db18819083e6cfbb597687c1 |
completed | May 11, 2026, 2:39 a.m. |
Created at: April 10, 2026, 5:36 a.m.