Triple
T29525877
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Man Who Mistook His Wife for a Hat (opera) |
E749066
|
entity |
| Predicate | hasLibrettoSource |
P191577
|
FINISHED |
| Object | Oliver Sacks’s clinical case study |
—
|
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: Oliver Sacks’s clinical case study | Statement: [The Man Who Mistook His Wife for a Hat (opera), hasLibrettoSource, Oliver Sacks’s clinical case study]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLibrettoSource Context triple: [The Man Who Mistook His Wife for a Hat (opera), hasLibrettoSource, Oliver Sacks’s clinical case study]
-
A.
hasLibrettoType
Indicates the specific type or category of libretto associated with a work or performance.
-
B.
hasLibrettoPublication
Indicates that a work is associated with a specific published version of its libretto.
-
C.
includesLibretto
Indicates that one entity (typically a musical or operatic work or publication) contains or is accompanied by the full text/libretto of another work.
-
D.
librettoBy
Indicates that a work’s libretto (the text of an opera or similar vocal work) was written by a particular person.
-
E.
originalLanguageOfLibretto
Indicates the language in which a libretto was originally written for a given work.
- 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_69f0bd46d99c81908ba9d01cc1dbef7d |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_69fce28d6c3081908bf76f5db63ecf68 |
completed | May 7, 2026, 7:05 p.m. |
| PD | Predicate disambiguation | batch_69fce12d2f08819082134b5eb3db6a24 |
completed | May 7, 2026, 6:59 p.m. |
| PDg | Predicate description generation | batch_69fce28a74508190aab36551094e8226 |
completed | May 7, 2026, 7:05 p.m. |
Created at: April 28, 2026, 4:45 p.m.