Triple
T975139
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beowulf |
E21035
|
entity |
| Predicate | approximateLineCount |
P7673
|
FINISHED |
| Object | 3182 lines |
—
|
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: 3182 lines | Statement: [Beowulf, approximateLineCount, 3182 lines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateLineCount Context triple: [Beowulf, approximateLineCount, 3182 lines]
-
A.
hasNumberOfLines
Indicates the relationship that specifies how many lines are associated with a given entity.
-
B.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
C.
approximateNumberOfVerses
chosen
Indicates an estimated or approximate count of verses associated with an entity.
-
D.
hasPageCountApprox
Indicates that an entity is associated with an approximate or estimated number of pages, rather than an exact page count.
-
E.
approximateSize
Indicates that one entity has a size that is roughly or approximately equal to the size of another entity.
- 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_69a493c2b62c8190b616351789ec47f8 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b460a5c0819087b03dfb8a3af2c2 |
completed | March 1, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a6aa2c8190aebba71320ab678f |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.