Triple
T7597064
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dark Blues |
E179882
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Oxford |
E19137
|
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: Oxford | Statement: [Dark Blues, location, Oxford]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oxford Context triple: [Dark Blues, location, Oxford]
-
A.
Oxford
Oxford is a small rural town in New Zealand’s Canterbury region, known for its farming community and proximity to the Southern Alps.
-
B.
Oxford
chosen
Oxford is a historic English city renowned for its prestigious university, distinctive architecture, and long-standing academic and cultural influence.
-
C.
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.
-
D.
Oxford
Oxford is a small town in New Haven County, Connecticut, known for its suburban-rural character and growing residential communities.
-
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 (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_69c69f3487ec8190bf7acdf2dd91e6d6 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9d4d3408190b9650347c7850c47 |
completed | March 27, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c861a3263481908a178b99e487bde5 |
completed | March 28, 2026, 11:17 p.m. |
Created at: March 27, 2026, 3:53 p.m.