Triple
T24169148
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xenia Township, Greene County, Ohio |
E599075
|
entity |
| Predicate | borderTypeWithXenia |
P155573
|
FINISHED |
| Object | surrounding jurisdiction |
—
|
LITERAL 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: surrounding jurisdiction | Statement: [Xenia Township, Greene County, Ohio, borderTypeWithXenia, surrounding jurisdiction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: borderTypeWithXenia Context triple: [Xenia Township, Greene County, Ohio, borderTypeWithXenia, surrounding jurisdiction]
-
A.
borderArea
Indicates that an area lies along or near the boundary between two regions, countries, or territories.
-
B.
borderWithin
Indicates that one region’s border lies entirely inside the boundary of another region.
-
C.
borderTypeWithChina
Indicates the type or nature of the border relationship an entity has with China.
-
D.
borderTypeWithRussia
Indicates the type or nature of a geographic border that an entity shares with Russia.
-
E.
borderTypeWithMongolia
Indicates the specific nature or classification of the border that an entity shares with Mongolia.
- F. None of above. chosen
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_69e288cbd62881909de32ca64a70c17b |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f27c9ddfcc819096697a844b300cce |
completed | April 29, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69f1c42f942c8190b103ff29a60fef34 |
completed | April 29, 2026, 8:41 a.m. |
| PDg | Predicate description generation | batch_69f27a753ca8819095706970d368f762 |
completed | April 29, 2026, 9:39 p.m. |
Created at: April 17, 2026, 11:33 p.m.