Triple
T30307581
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hasekura Tsunenaga |
E770826
|
entity |
| Predicate | audienceWith |
P169067
|
FINISHED |
| Object | Pope Paul V |
—
|
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: Pope Paul V | Statement: [Hasekura Tsunenaga, audienceWith, Pope Paul V]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: audienceWith Context triple: [Hasekura Tsunenaga, audienceWith, Pope Paul V]
-
A.
audienceDescribedAs
Indicates that an audience is characterized or labeled using a particular description or set of attributes.
-
B.
audienceMember
Indicates that one entity is an audience member who is attending, observing, or listening to a performance, event, or presentation involving the other entity.
-
C.
audienceWears
Indicates that members of an audience are wearing or have on a particular item or type of clothing or accessory.
-
D.
audienceSetting
Indicates the context or environment in which an audience is situated or addressed.
-
E.
audienceImpact
Indicates how an action, message, or event affects, influences, or resonates with its intended audience.
- 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_69f22488f224819081b0f3ec41ab975c |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6816966e8819094d81abb060be372 |
completed | May 2, 2026, 10:57 p.m. |
| PD | Predicate disambiguation | batch_69f6760216108190bbb708d53a6c2c25 |
completed | May 2, 2026, 10:09 p.m. |
| PDg | Predicate description generation | batch_69f676c35f3481909b9ba18a5662d6ce |
completed | May 2, 2026, 10:12 p.m. |
Created at: April 29, 2026, 7:49 p.m.