Triple
T1468172
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DANMARC2 |
E27071
|
entity |
| Predicate | hasTypeOfContent |
P640
|
FINISHED |
| Object | machine-readable cataloging data |
—
|
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: machine-readable cataloging data | Statement: [DANMARC2, hasTypeOfContent, machine-readable cataloging data]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfContent Context triple: [DANMARC2, hasTypeOfContent, machine-readable cataloging data]
-
A.
hasContentType
chosen
Indicates that an entity is associated with or classified by a specific type of content.
-
B.
hasTypeOfResource
Indicates that an entity is associated with a specific category or kind of resource it represents or utilizes.
-
C.
haveType
Indicates that an entity belongs to or is classified under a specified type or category.
-
D.
hasContentFrom
Indicates that one entity’s content is derived from, includes, or is based on another entity.
-
E.
offersContentType
Indicates that one entity provides or makes available content of a specified type to another entity or context.
- 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_69a496d25d6881909dbd84f86d763992 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c5d70a948190b50a6c1b36abc740 |
completed | March 1, 2026, 11:03 p.m. |
| PD | Predicate disambiguation | batch_69a4c48350d88190a81bd149103f93e3 |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8:01 p.m.