Triple
T13723408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Moore in TrumpLand |
E329092
|
entity |
| Predicate | locationOfFilming |
P4373
|
FINISHED |
| Object | Wilmington, Ohio |
—
|
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: Wilmington, Ohio | Statement: [Michael Moore in TrumpLand, locationOfFilming, Wilmington, Ohio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wilmington, Ohio Context triple: [Michael Moore in TrumpLand, locationOfFilming, Wilmington, Ohio]
-
A.
Wilmington, Ohio
chosen
Wilmington, Ohio is a small city in southwestern Ohio known historically as a regional transportation hub and home to a major air park and agricultural community.
-
B.
New London, Ohio
New London, Ohio is a small village in Huron County known for its rural character and location in north-central Ohio.
-
C.
Harrisburg, Ohio
Harrisburg, Ohio is a small village in central Ohio that functions as part of the Columbus metropolitan area.
-
D.
Hartford, Ohio
Hartford, Ohio is a small rural village located in central Ohio within Licking County.
-
E.
Trenton, Ohio
Trenton, Ohio is a small city in Butler County known as a residential community within the greater Cincinnati–Dayton metropolitan region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69de01f52e748190b49c34e10ab8ac34 |
completed | April 14, 2026, 8:59 a.m. |
Created at: April 9, 2026, 9:55 p.m.