Triple
T5385718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | motaqareb metre |
E120196
|
entity |
| Predicate | typicalFootCountPerHemistich |
P62939
|
FINISHED |
| Object | four feet |
—
|
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: four feet | Statement: [motaqareb metre, typicalFootCountPerHemistich, four feet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalFootCountPerHemistich Context triple: [motaqareb metre, typicalFootCountPerHemistich, four feet]
-
A.
numberOfOfficialStanzas
Indicates the total count of officially recognized stanzas associated with an entity, such as a song, poem, or anthem.
-
B.
numberOfStanzasInOriginalPoem
Indicates the total count of stanzas contained in the poem’s original version.
-
C.
rhymeScheme
Indicates the pattern of end sounds in a sequence of lines, showing which lines rhyme with each other.
-
D.
hasStandardToeCount
Indicates that an entity possesses the typical or expected number of toes for its kind.
-
E.
typicalMeterInEnglish
Indicates that a given poetic meter is commonly or characteristically used in English verse.
- 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_69bd46354c648190a38b26f107010a96 |
completed | March 20, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69bd86f5a7388190aa4ba2052afca74e |
completed | March 20, 2026, 5:42 p.m. |
| PD | Predicate disambiguation | batch_69bd8463a9c88190bd760378f3026180 |
completed | March 20, 2026, 5:31 p.m. |
| PDg | Predicate description generation | batch_69bd853005088190b1b092a9beb090b2 |
completed | March 20, 2026, 5:34 p.m. |
Created at: March 20, 2026, 2:03 p.m.