Triple
T19490646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christian Müller |
E487638
|
entity |
| Predicate | instrumentTypeSpecializedIn |
P89239
|
FINISHED |
| Object | pipe organ |
—
|
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: pipe organ | Statement: [Christian Müller, instrumentTypeSpecializedIn, pipe organ]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: instrumentTypeSpecializedIn Context triple: [Christian Müller, instrumentTypeSpecializedIn, pipe organ]
-
A.
isSpecializedFor
chosen
Indicates that one entity is specifically adapted, designed, or focused to perform optimally for a particular function, context, or domain associated with another entity.
-
B.
issuerSpecialization
Indicates the particular field, domain, or area of expertise in which the issuer is specialized.
-
C.
relatedInstrumentType
Indicates that one instrument is associated with or connected to another by type, such as being a variant, subtype, or closely related form.
-
D.
hasFinancialInstrumentType
Indicates that an entity is associated with, or classified by, a specific type or category of financial instrument.
-
E.
craftSpecialty
Indicates that an entity has a particular area of specialized skill or focus within a craft or artisanal practice.
- 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_69d8e8d9d1c88190b01cd78b8be49384 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6348d43448190bf83680522b8b415 |
completed | April 20, 2026, 2:13 p.m. |
| PD | Predicate disambiguation | batch_69e4fd7883308190b73912a71a35a835 |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:39 p.m.