Triple
T6131610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tobermory Distillery |
E136730
|
entity |
| Predicate | hasMashTunType |
P69350
|
FINISHED |
| Object | traditional copper-topped mash tun |
—
|
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: traditional copper-topped mash tun | Statement: [Tobermory Distillery, hasMashTunType, traditional copper-topped mash tun]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMashTunType Context triple: [Tobermory Distillery, hasMashTunType, traditional copper-topped mash tun]
-
A.
hasTankType
Indicates that an entity is associated with, or classified by, a specific type or category of tank.
-
B.
hasGrainType
Indicates that an entity is characterized by or associated with a specific type of grain.
-
C.
hasSternType
Indicates that an entity (typically a vessel) possesses a specific type or design of stern.
-
D.
hasTeeType
Indicates that an entity (typically a golf hole or course) is associated with a specific type or category of tee.
-
E.
hasBenchType
Indicates that an entity is associated with or characterized by a specific type or category of bench.
- 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_69c008a0a37c81908e5b4f879158afb3 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05c4f7ad8819096b09ddd312453fb |
completed | March 22, 2026, 9:17 p.m. |
| PD | Predicate disambiguation | batch_69c055f19b0c81908be34a00ab218723 |
completed | March 22, 2026, 8:49 p.m. |
| PDg | Predicate description generation | batch_69c056c87340819088003f427706ebf8 |
completed | March 22, 2026, 8:53 p.m. |
Created at: March 22, 2026, 4:15 p.m.