Triple
T9422889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Salian Franks |
E227197
|
entity |
| Predicate | modernEquivalentOfToxandria |
P21626
|
FINISHED |
| Object | parts of the southern Netherlands and northern Belgium |
—
|
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: parts of the southern Netherlands and northern Belgium | Statement: [Salian Franks, modernEquivalentOfToxandria, parts of the southern Netherlands and northern Belgium]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: modernEquivalentOfToxandria Context triple: [Salian Franks, modernEquivalentOfToxandria, parts of the southern Netherlands and northern Belgium]
-
A.
modernEquivalent
chosen
Indicates that one entity serves as the contemporary or updated counterpart of another earlier or traditional entity.
-
B.
toxicTo
Indicates that one entity causes harm, poisoning, or adverse effects to another when exposed or applied.
-
C.
toxinType
Indicates the specific kind or category of toxin associated with an entity.
-
D.
toxinEffect
Indicates the harmful impact or physiological response caused by a toxin on a target entity.
-
E.
modernUse
Indicates how something is currently used or applied in modern times.
- 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_69ca8436ba308190903e470776d2d893 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd6c27c8cc8190a11162c10c33b17e |
completed | April 1, 2026, 7:04 p.m. |
| PD | Predicate disambiguation | batch_69cca550777c819094e1851a6127cbbc |
completed | April 1, 2026, 4:55 a.m. |
Created at: March 30, 2026, 7:48 p.m.