Triple
T1498967
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jèrriais |
E29750
|
entity |
| Predicate | hasFirstDocumentedUse |
P28519
|
FINISHED |
| Object | 13th century |
—
|
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: 13th century | Statement: [Jèrriais, hasFirstDocumentedUse, 13th century]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFirstDocumentedUse Context triple: [Jèrriais, hasFirstDocumentedUse, 13th century]
-
A.
hasHumanUse
Indicates that something is used, employed, or utilized by humans for a particular purpose or benefit.
-
B.
hasLimitedDocumentation
Indicates that the subject is associated with documentation that is sparse, incomplete, or not sufficiently detailed.
-
C.
hasDocumentationAt
Indicates that something is documented or described in a specific document, file, or location.
-
D.
firstUsedFor
Indicates that one entity was the earliest or original thing for which another entity was used or applied.
-
E.
hasGivenNameUsage
Indicates that an entity is associated with a particular way or context in which its given name is used.
- 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_69a498dba1d8819093b46a3a8d2485f1 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c6f0ce988190aafab4a6e0dfd710 |
completed | March 1, 2026, 11:08 p.m. |
| PD | Predicate disambiguation | batch_69a4c48a8cf48190a6ebf8d44a608a06 |
completed | March 1, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69a4c4feea448190b2b5071b28a5b608 |
completed | March 1, 2026, 11 p.m. |
Created at: March 1, 2026, 8:12 p.m.