Triple
T33160279
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LNER Class A4 4498 Sir Nigel Gresley |
E848709
|
entity |
| Predicate | hasHeadboard |
P193374
|
FINISHED |
| Object | The Elizabethan |
—
|
NE NERFINISHED |
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: The Elizabethan | Statement: [LNER Class A4 4498 Sir Nigel Gresley, hasHeadboard, The Elizabethan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHeadboard Context triple: [LNER Class A4 4498 Sir Nigel Gresley, hasHeadboard, The Elizabethan]
-
A.
hasBackrest
Indicates that one entity (typically a seat or seating object) includes or is equipped with a supporting backrest.
-
B.
hasBackrestType
Indicates the specific kind or style of backrest that an object (typically a seat or chair) possesses.
-
C.
hasCabinet
Indicates that one entity possesses, includes, or is equipped with a cabinet associated with it.
-
D.
hasChairs
Indicates that one entity possesses, contains, or is equipped with one or more chairs.
-
E.
chairType
Indicates the specific kind or category of chair that an entity is classified as.
- 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_69f3495b02d08190bb3d366823dffc21 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fd44474ed48190ac372e4c88d762ed |
completed | May 8, 2026, 2:02 a.m. |
| PD | Predicate disambiguation | batch_69fd41ef28a48190a66959be5c964461 |
completed | May 8, 2026, 1:52 a.m. |
| PDg | Predicate description generation | batch_69fd4445f8c08190bb2dc27e0971c55d |
completed | May 8, 2026, 2:02 a.m. |
Created at: May 1, 2026, 1:28 a.m.