Triple
T2067378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berliner gramophone |
E45933
|
entity |
| Predicate | materialOfEarlyDiscs |
P1272
|
FINISHED |
| Object | hard rubber |
—
|
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: hard rubber | Statement: [Berliner gramophone, materialOfEarlyDiscs, hard rubber]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: materialOfEarlyDiscs Context triple: [Berliner gramophone, materialOfEarlyDiscs, hard rubber]
-
A.
opticalDiscType
Indicates the specific kind or category of an optical disc involved in the relationship (e.g., CD, DVD, Blu-ray).
-
B.
materialUsed
chosen
Indicates that one entity is made from, incorporates, or utilizes the other entity as its material or substance.
-
C.
phonographicType
Indicates the specific kind or category of phonographic recording or medium associated with an entity.
-
D.
numberOfDiscs
Indicates the quantity of discs associated with or contained by a given entity.
-
E.
sourceMaterialType
Indicates the type or category of material from which something originates or is derived.
- 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_69a8891b38288190abd572ccad9b6928 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb9f236f08190b602d337afb1880f |
completed | March 7, 2026, 5:38 a.m. |
| PD | Predicate disambiguation | batch_69abb7aee9b48190999620176e3a6ee2 |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:40 p.m.