Triple
T19736295
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hyakunin Isshu |
E473990
|
entity |
| Predicate | timeSpanOfPoems |
P137137
|
FINISHED |
| Object | 7th century to 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: 7th century to 13th century | Statement: [Hyakunin Isshu, timeSpanOfPoems, 7th century to 13th century]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeSpanOfPoems Context triple: [Hyakunin Isshu, timeSpanOfPoems, 7th century to 13th century]
-
A.
poemLength
Indicates the length or extent of a poem, typically measured in units such as lines, verses, or words.
-
B.
poemLengthRange
Indicates the range of acceptable or actual lengths (e.g., in lines, words, or characters) associated with a poem.
-
C.
approximateNumberOfPoems
Indicates an estimated or roughly calculated count of poems associated with an entity.
-
D.
containsNumberOfPoems
Indicates that one entity includes or specifies a particular quantity of poems associated with it.
-
E.
numberOfPoets
Indicates the quantity or count of poets associated with a given entity or context.
- 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_69d8e517ebd48190979ee76723bcfadf |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6515ddea881909ea831b7bc16d934 |
completed | April 20, 2026, 4:16 p.m. |
| PD | Predicate disambiguation | batch_69e5304a7aac8190ac13f75f0c008e45 |
completed | April 19, 2026, 7:43 p.m. |
| PDg | Predicate description generation | batch_69e532bbedf081908d801600e2af94a7 |
completed | April 19, 2026, 7:53 p.m. |
Created at: April 10, 2026, 1:47 p.m.