Triple
T15269555
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Australian National Bibliographic Database |
E364984
|
entity |
| Predicate | coversMaterialType |
P117898
|
FINISHED |
| Object | books |
—
|
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: books | Statement: [Australian National Bibliographic Database, coversMaterialType, books]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coversMaterialType Context triple: [Australian National Bibliographic Database, coversMaterialType, books]
-
A.
featuresMaterialType
Indicates that an entity is characterized by or incorporates a specific type of material.
-
B.
suppliedMaterialType
Indicates the type or category of material that is provided or supplied in a given context.
-
C.
hasMaterialType
Indicates that something is composed of, made from, or characterized by a specific type of material.
-
D.
sourceMaterialType
Indicates the type or category of material from which something originates or is derived.
-
E.
collectsMaterialType
Indicates that an entity gathers or acquires materials of a specified type.
- 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_69d85a0f08408190b3c3259ae35d79d2 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0094eac848190a1740ae1aa6b28e0 |
completed | April 15, 2026, 9:55 p.m. |
| PD | Predicate disambiguation | batch_69deca90739081909bd1b797cdb8af2b |
completed | April 14, 2026, 11:15 p.m. |
| PDg | Predicate description generation | batch_69decf2ca6148190967c319728ec3661 |
completed | April 14, 2026, 11:35 p.m. |
Created at: April 10, 2026, 3:14 a.m.