Triple
T26944285
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Disney Treasure |
E678595
|
entity |
| Predicate | hasGrossTonnage |
P160604
|
FINISHED |
| Object | approximately 144000 GT |
—
|
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: approximately 144000 GT | Statement: [Disney Treasure, hasGrossTonnage, approximately 144000 GT]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGrossTonnage Context triple: [Disney Treasure, hasGrossTonnage, approximately 144000 GT]
-
A.
grossTonnage
Indicates the total internal volume or carrying capacity of a vessel, measured in gross tons, as defined by maritime tonnage rules.
-
B.
tonnageGrossRegisterTonsApproximate
chosen
Indicates that the gross register tonnage of something (typically a vessel) is given as an approximate value rather than an exact measurement.
-
C.
maxVesselTonnage
Indicates the maximum tonnage capacity that a vessel is allowed or designed to carry.
-
D.
tonnageClass
Indicates a classification relationship where an entity is assigned to a category based on its tonnage (weight or carrying capacity range).
-
E.
deadweightTonnage
Indicates the total carrying capacity of a vessel, measured as the maximum weight of cargo, fuel, passengers, provisions, and other loads it can safely transport.
- 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_69eeeb4d69588190a7c912164a1c37b3 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f65f7731e4819099d5bd3d915ee266 |
completed | May 2, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f65c1f94ac8190bc6fbc7916fc0d82 |
completed | May 2, 2026, 8:18 p.m. |
Created at: April 27, 2026, 6:20 a.m.