Triple
T18452550
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anne Blunt, 15th Baroness Wentworth |
E450820
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Blunt |
—
|
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: Blunt | Statement: [Anne Blunt, 15th Baroness Wentworth, familyName, Blunt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blunt Context triple: [Anne Blunt, 15th Baroness Wentworth, familyName, Blunt]
-
A.
Blunt
chosen
Blunt is an English surname borne by various notable figures in the arts, politics, and public life.
-
B.
Blunt Talk
Blunt Talk is a satirical American television comedy series centered on a British newscaster navigating personal and professional chaos in Los Angeles.
-
C.
Bland
Bland is an English-language surname borne by various notable individuals across politics, sports, the arts, and other fields.
-
D.
Sharp
Sharp is a Japanese electronics manufacturer best known for producing consumer devices such as mobile phones, televisions, and display technologies.
-
E.
Sharp
Sharp is a common English surname borne by numerous notable individuals across politics, sports, academia, and the arts.
- 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_69d8d38345688190b565eac2e4cd7935 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5264906cc8190b8b2cb77ec93d480 |
completed | April 19, 2026, 7 p.m. |
Created at: April 10, 2026, 11:31 a.m.