Triple
T20227968
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hart Publishing |
E495441
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Oxford |
—
|
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: Oxford | Statement: [Hart Publishing, location, Oxford]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oxford Context triple: [Hart Publishing, location, Oxford]
-
A.
Oxford
chosen
Oxford is a historic English city renowned for its prestigious university, distinctive architecture, and long-standing academic and cultural influence.
-
B.
Oxford
Oxford is a small city in northeastern Alabama known for its location in the Anniston–Oxford metropolitan area and proximity to the Talladega National Forest.
-
C.
Oxford
Oxford is a small rural town in New Zealand’s Canterbury region, known for its farming community and proximity to the Southern Alps.
-
D.
Oxford
Oxford is a small Mississippi city best known as the home of the University of Mississippi and for its rich literary and cultural heritage.
-
E.
Oxford
Oxford is a small borough in southeastern Pennsylvania known for its historic downtown and proximity to several colleges and rural communities.
- 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_69da626cff80819097b530718a7c98b6 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66fdb61b08190b850a9648ebfb720 |
completed | April 20, 2026, 6:26 p.m. |
Created at: April 11, 2026, 11:39 p.m.