Triple
T32887886
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schultze |
E841249
|
entity |
| Predicate | hasTypicalOriginCountry |
P103556
|
FINISHED |
| Object | Germany |
—
|
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: Germany | Statement: [Schultze, hasTypicalOriginCountry, Germany]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalOriginCountry Context triple: [Schultze, hasTypicalOriginCountry, Germany]
-
A.
hasTypicalGeographicOrigin
chosen
Indicates that an entity is commonly or characteristically associated with originating from a particular geographic location.
-
B.
basedOnWorkCountryOfOrigin
Indicates that something is determined or derived from the country of origin of a work.
-
C.
countryOfOrigin
Indicates the country from which an entity originally comes or was first produced, created, or established.
-
D.
likelyCountryOfOrigin
Indicates that an entity is probably from, or most commonly associated with originating in, a particular country.
-
E.
organizationCountryOfOrigin
Indicates the country where an organization was originally founded or established.
- 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_69f349446e288190a70c05bcc4d81172 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fedfd913f48190bdcd450980868d9a |
completed | May 9, 2026, 7:18 a.m. |
| PD | Predicate disambiguation | batch_69fedf58c6e88190821a7156054c9086 |
completed | May 9, 2026, 7:16 a.m. |
Created at: May 1, 2026, 1:18 a.m.