Triple
T21089373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Edward Hicks |
E519591
|
entity |
| Predicate | numberOfVersionsOf"The Peaceable Kingdom" |
P142796
|
FINISHED |
| Object | over 60 |
—
|
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: over 60 | Statement: [Edward Hicks, numberOfVersionsOf"The Peaceable Kingdom", over 60]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfVersionsOf"The Peaceable Kingdom" Context triple: [Edward Hicks, numberOfVersionsOf"The Peaceable Kingdom", over 60]
-
A.
numberOfEditions
Indicates the total count of distinct editions associated with a given entity.
-
B.
عدد الحروف
Indicates the relationship that specifies the number of letters contained in a given word or text.
-
C.
hasPoemOrRhymeVersion
Indicates that one entity is a poem or rhyme version, adaptation, or variant of another entity.
-
D.
عدد المقاطع
Indicates the number of segments or parts into which something is divided.
-
E.
hasVerseCount
Indicates that an entity (such as a text or section) is associated with a specific number of verses it contains.
- 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_69e0b507dd9081908fb8bfcbef4c8b46 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7094dd65481909391ed74115afc23 |
completed | April 21, 2026, 5:21 a.m. |
| PD | Predicate disambiguation | batch_69e5dbfcd5e881908f1e4e0d2d237856 |
completed | April 20, 2026, 7:55 a.m. |
| PDg | Predicate description generation | batch_69e5e2e03d88819086f8b641656ad8b0 |
completed | April 20, 2026, 8:25 a.m. |
Created at: April 16, 2026, 2:50 p.m.