Triple
T14411666
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French horn |
E357341
|
entity |
| Predicate | commonType |
P32595
|
FINISHED |
| Object | double horn |
—
|
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: double horn | Statement: [French horn, commonType, double horn]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commonType Context triple: [French horn, commonType, double horn]
-
A.
commonIn
Indicates that something frequently occurs, appears, or is found within a specified context, group, or environment.
-
B.
commonFor
chosen
Indicates that something is typical, usual, or frequently occurring for a given entity or context.
-
C.
commonOn
Indicates that two or more entities share the same location, context, or medium where they are present or occur together.
-
D.
commonStructure
Indicates that two or more entities share the same or a very similar internal organization, pattern, or arrangement.
-
E.
commonCut
Indicates that two or more entities share at least one identical segment or portion that has been cut or divided in the same way.
- 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_69d82793421c8190861eb0e673b085de |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de90c9b3448190aec1608836a5e913 |
completed | April 14, 2026, 7:08 p.m. |
| PD | Predicate disambiguation | batch_69de2aa1b57881909a033eac8545c417 |
completed | April 14, 2026, 11:53 a.m. |
Created at: April 10, 2026, 1:17 a.m.