Triple
T651391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malchuyot |
E11351
|
entity |
| Predicate | typicalVerseCount |
P7673
|
FINISHED |
| Object | ten verses |
—
|
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: ten verses | Statement: [Malchuyot, typicalVerseCount, ten verses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalVerseCount Context triple: [Malchuyot, typicalVerseCount, ten verses]
-
A.
hasVerseCount
Indicates that an entity (such as a text or section) is associated with a specific number of verses it contains.
-
B.
approximateNumberOfVerses
chosen
Indicates an estimated or approximate count of verses associated with an entity.
-
C.
numberOfStanzasInOriginalPoem
Indicates the total count of stanzas contained in the poem’s original version.
-
D.
numberOfOfficialStanzas
Indicates the total count of officially recognized stanzas associated with an entity, such as a song, poem, or anthem.
-
E.
mostVersesChapter
Indicates that a chapter has the highest number of verses compared to all other chapters within the same collection or text.
- 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_69a493266a2881909daf4c40f719dee8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f33b6d881908b6662b73d6fe833 |
completed | March 1, 2026, 8:19 p.m. |
| PD | Predicate disambiguation | batch_69a49d0eade081909c47e85ed55f808d |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:36 p.m.