Triple
T9963979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Elizabethan |
E195634
|
entity |
| Predicate | trainClass |
P56947
|
FINISHED |
| Object | first‑class and second‑class accommodation |
—
|
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: first‑class and second‑class accommodation | Statement: [The Elizabethan, trainClass, first‑class and second‑class accommodation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainClass Context triple: [The Elizabethan, trainClass, first‑class and second‑class accommodation]
-
A.
railroadClass
Indicates the classification or category of a railroad according to an established system (e.g., by size, revenue, or regulatory status).
-
B.
trainsCategory
Indicates that one entity is a category or type under which the other entity is trained or classified.
-
C.
trainTypeUsed
chosen
Indicates that a specific type or category of train is employed or operated in a given context or service.
-
D.
trainsetType
Indicates the specific category or role of a dataset within a training process (e.g., training, validation, or test set).
-
E.
trains
Indicates that one entity teaches, instructs, or coaches another entity to develop skills, knowledge, or abilities.
- 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_69ca82ebd1288190912f9e4482d1fa35 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb71a33b48190a18c1a9023f249d2 |
completed | April 2, 2026, 12:23 a.m. |
| PD | Predicate disambiguation | batch_69cd1d9ae19c819099fb3635e57c79be |
completed | April 1, 2026, 1:28 p.m. |
Created at: March 30, 2026, 8:47 p.m.