Triple
T13358971
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kent coalfield |
E318772
|
entity |
| Predicate | firstShaftsSunk |
P109612
|
FINISHED |
| Object | 1890s |
—
|
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: 1890s | Statement: [Kent coalfield, firstShaftsSunk, 1890s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstShaftsSunk Context triple: [Kent coalfield, firstShaftsSunk, 1890s]
-
A.
tonnageSunk
Indicates the amount of a vessel’s weight or cargo capacity that has been destroyed or sunk, typically measured in tons.
-
B.
shipwrecksDestroyedIn
Indicates that one or more shipwrecks were destroyed within a specified location or during a particular event or time period.
-
C.
shipsSunkOrTotalLoss
Indicates that the referenced ships were sunk or otherwise rendered a total loss (permanently unusable).
-
D.
sunkBy
Indicates that one entity (typically a vessel or structure) was caused to sink or be destroyed in water by another entity.
-
E.
shipTypeSunk
Indicates that a particular type of ship has been sunk as a result of some event or action.
- 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_69d806b7bbac8190b85278c87fa7aff3 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69da62887e588190bd7241c720a112a2 |
completed | April 11, 2026, 3:02 p.m. |
| PD | Predicate disambiguation | batch_69d9a02c9abc8190b328e7bae747bfc5 |
completed | April 11, 2026, 1:13 a.m. |
| PDg | Predicate description generation | batch_69dadcce5a808190847f2a7833b67a5a |
completed | April 11, 2026, 11:44 p.m. |
Created at: April 9, 2026, 9:32 p.m.