Triple
T19965892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duchy of Brittany |
E479931
|
entity |
| Predicate | notableRuler |
P22
|
FINISHED |
| Object | Nominoe |
—
|
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: Nominoe | Statement: [Duchy of Brittany, notableRuler, Nominoe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nominoe Context triple: [Duchy of Brittany, notableRuler, Nominoe]
-
A.
Nominoe
chosen
Nominoe was a 9th-century Breton leader often regarded as the founder of an independent Brittany for his successful resistance against Frankish rule.
-
B.
Omi
Omi is the central protagonist of the 2015 Indian romantic drama film "Love," around whom the story’s emotional and narrative arc revolves.
-
C.
Masaru
Masaru is a Japanese given name commonly used for males and borne by various notable figures in fields such as technology, sports, and entertainment.
-
D.
Nanisca
Nanisca is a fierce and strategic general of the all-female Agojie warriors in the historical epic film "The Woman King."
-
E.
Seishi
Seishi is a Japanese given name historically borne by figures such as Fujiwara no Seishi, a noblewoman of the Heian period.
- 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_69d8e523c19881909f9197037200dde6 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65bc4f47c8190a721f5e488150d81 |
completed | April 20, 2026, 5 p.m. |
Created at: April 10, 2026, 1:54 p.m.