Triple
T17218911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Young Prince and the Young Princess |
E417922
|
entity |
| Predicate | opusNumberOfWholeWork |
P92730
|
FINISHED |
| Object | Op. 35 |
—
|
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: Op. 35 | Statement: [The Young Prince and the Young Princess, opusNumberOfWholeWork, Op. 35]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: opusNumberOfWholeWork Context triple: [The Young Prince and the Young Princess, opusNumberOfWholeWork, Op. 35]
-
A.
numberOfWorks
Indicates the total count of works associated with a given entity.
-
B.
hasNumberOfPartsInWholeWork
Indicates that an entity specifies how many component parts are contained within a complete work.
-
C.
seriesNumberOfWorks
Indicates that an entity is assigned a specific position or sequence number within a series of related works.
-
D.
numberOfWorksCreated
Indicates the total count of creative works that an entity has produced or authored.
-
E.
authorWorkNumber
chosen
Indicates the specific ordinal number or position of a work within an author's body of works.
- 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_69d886d779488190b131369541c04e7d |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42ddc3cb88190a67e35164d710d9d |
completed | April 19, 2026, 1:20 a.m. |
| PD | Predicate disambiguation | batch_69e3831e354881908c5505ffd15c84e9 |
completed | April 18, 2026, 1:11 p.m. |
Created at: April 10, 2026, 5:38 a.m.