Triple
T3274154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RV Kilo Moana |
E68718
|
entity |
| Predicate | totalBerths |
P27883
|
FINISHED |
| Object | approximately 48 persons |
—
|
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 48 persons | Statement: [RV Kilo Moana, totalBerths, approximately 48 persons]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalBerths Context triple: [RV Kilo Moana, totalBerths, approximately 48 persons]
-
A.
hasBerths
chosen
Indicates that one entity provides or contains sleeping or docking berths for another entity.
-
B.
wildCardBerthsCount
Indicates the number of wildcard berths (extra or non-standard qualification spots) allocated in a competition or selection process.
-
C.
berthType
Indicates the specific kind or category of berth associated with an entity, such as the type of sleeping or docking space provided.
-
D.
hasNumberOfFerrySlips
Indicates the specific count of ferry slips associated with or available at a given entity.
-
E.
hasBerthDepth
Indicates the depth of water available at a specific berth where a vessel can be moored.
- 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_69ad859b54f881909bf530d549caf2fd |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adaff74af88190809313743b439ff0 |
completed | March 8, 2026, 5:20 p.m. |
| PD | Predicate disambiguation | batch_69ada420167c81909b6e2702db296d9e |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:10 p.m.