Triple
T9520746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Sleeping Beauty |
E229636
|
entity |
| Predicate | prologueCount |
P88518
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [The Sleeping Beauty, prologueCount, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: prologueCount Context triple: [The Sleeping Beauty, prologueCount, 1]
-
A.
numberOfPropositions
Indicates the total count of distinct propositions associated with or contained within a given entity or context.
-
B.
sectionCountApproximate
Indicates that the number of sections associated with an entity is known only approximately rather than as an exact count.
-
C.
branchCount
Indicates the number of branches associated with a given entity or structure.
-
D.
phaseCount
Indicates the number of distinct phases or stages associated with a given process, event, or entity.
-
E.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
- 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_69ca847870a881909d8d751a7d29da39 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9884dd5c8190b69c178cb2ac75c2 |
completed | April 1, 2026, 10:13 p.m. |
| PD | Predicate disambiguation | batch_69cca56a3d088190bdc16670678fb6c6 |
completed | April 1, 2026, 4:56 a.m. |
| PDg | Predicate description generation | batch_69cca89f1d748190bf3636bea28d8a37 |
completed | April 1, 2026, 5:09 a.m. |
Created at: March 30, 2026, 7:59 p.m.