Triple
T38430721
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arnold von Winkelried |
E903790
|
entity |
| Predicate | firstClearLiteraryMention |
P3921
|
FINISHED |
| Object | 16th 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: 16th century | Statement: [Arnold von Winkelried, firstClearLiteraryMention, 16th century]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstClearLiteraryMention Context triple: [Arnold von Winkelried, firstClearLiteraryMention, 16th century]
-
A.
firstMajorLiteraryUseDate
Indicates the date when something was first significantly used or attested in major literary works.
-
B.
firstMajorLiteraryAppearanceAuthor
Indicates the author responsible for the work in which an entity made its first major literary appearance.
-
C.
firstExplicitlyMentionedBy
Indicates that one entity is the earliest known source to explicitly mention or refer to another entity.
-
D.
firstClearlyAttestedIn
chosen
Indicates the earliest known point in time or source where something is clearly documented or evidenced.
-
E.
firstClearlyRecognizedIn
Indicates the point in time or context when something was first clearly identified, acknowledged, or distinguished as such.
- 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_69f76e6a2024819081aa04f4932f89d2 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1e32108190897356d6a7fed879 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:31 p.m.