Triple
T16001395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Schmidt |
E388100
|
entity |
| Predicate | givenNameOrigin |
P3325
|
FINISHED |
| Object | Hebrew origin via Biblical name Michael |
—
|
LITERAL 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: Hebrew origin via Biblical name Michael | Statement: [Michael Schmidt, givenNameOrigin, Hebrew origin via Biblical name Michael]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: givenNameOrigin Context triple: [Michael Schmidt, givenNameOrigin, Hebrew origin via Biblical name Michael]
-
A.
hasNameOrigin
chosen
Indicates that the origin or source of an entity’s name is specified by the related entity.
-
B.
givenNameFor
Indicates that one entity is the personal first name assigned to or used for another entity.
-
C.
givenNameBy
Indicates that one entity has been assigned or provided a specific given (first) name by another entity.
-
D.
givenName
Indicates the personal first name assigned to an individual.
-
E.
givenNameUsageCountry
Indicates the country in which a particular given name is used or commonly found.
- F. None of above.
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_69d86daa562c81908aacc179c0fe8fb5 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142dc081c819082527e3fa8773460 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:55 a.m.