Triple
T4757747
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | terza rima |
E105628
|
entity |
| Predicate | typicalMeterInItalian |
P58308
|
FINISHED |
| Object | hendecasyllabic line |
—
|
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: hendecasyllabic line | Statement: [terza rima, typicalMeterInItalian, hendecasyllabic line]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalMeterInItalian Context triple: [terza rima, typicalMeterInItalian, hendecasyllabic line]
-
A.
meter
Indicates a measurement relationship where one entity quantifies the length, distance, or extent of another in meters.
-
B.
typicalTempoControl
Indicates that one entity serves as the usual or standard means by which the tempo of another entity (such as a process or activity) is regulated or controlled.
-
C.
metre
Indicates a measurement relationship where one entity’s length, distance, or size is quantified in units of metres.
-
D.
meterSystem
Indicates that one entity uses, is measured in, or is associated with a particular system of meters or measurement units.
-
E.
typicalSubmultiples
Indicates that one quantity represents a standard or commonly used fractional multiple of another quantity (e.g., milli-, micro-, kilo- as typical submultiples).
- 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_69bd43f14cac819081c7c69803648211 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd650ad0f88190844bfcb46b3071c2 |
completed | March 20, 2026, 3:17 p.m. |
| PD | Predicate disambiguation | batch_69bd6225c9488190afee5bb3619d0365 |
completed | March 20, 2026, 3:05 p.m. |
| PDg | Predicate description generation | batch_69bd631328fc81909b28ae0a2a3ed9bb |
completed | March 20, 2026, 3:09 p.m. |
Created at: March 20, 2026, 1:20 p.m.