Triple
T26299925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grand Slam |
E661527
|
entity |
| Predicate | specificCarrierVariant |
P164831
|
FINISHED |
| Object | Lancaster B.Mk I Special |
—
|
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: Lancaster B.Mk I Special | Statement: [Grand Slam, specificCarrierVariant, Lancaster B.Mk I Special]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: specificCarrierVariant Context triple: [Grand Slam, specificCarrierVariant, Lancaster B.Mk I Special]
-
A.
specificCarrierAircraftVariant
Indicates that one aircraft variant is a specific version designed or adapted for carrier-based operations of another, more general aircraft variant.
-
B.
carrierExclusiveVariant
Indicates that a variant is available only through a specific carrier and not through others.
-
C.
carTypeVariant
Indicates that one car type is a specific variant or version of another car type.
-
D.
carrierType
Indicates the type or category of carrier involved in the relationship or action (e.g., the kind of entity that carries or transports something).
-
E.
shipClassVariant
Indicates that one ship class is a variant or modified version derived from another ship class.
- 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_69ee812cd48c81908054068f545f0526 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f650fc44e48190bc0e0a935eac62a6 |
completed | May 2, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69f64cab1f648190a2a9460690d18a37 |
completed | May 2, 2026, 7:12 p.m. |
| PDg | Predicate description generation | batch_69f650c466b881908954e43bfebae8a4 |
completed | May 2, 2026, 7:30 p.m. |
Created at: April 26, 2026, 10:15 p.m.