Triple
T25202925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port of Ilwaco |
E631167
|
entity |
| Predicate | hasApproximateBerthCount |
P82090
|
FINISHED |
| Object | several hundred slips |
—
|
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: several hundred slips | Statement: [Port of Ilwaco, hasApproximateBerthCount, several hundred slips]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateBerthCount Context triple: [Port of Ilwaco, hasApproximateBerthCount, several hundred slips]
-
A.
numberOfBerths
chosen
Indicates the quantity of berths (sleeping places or docking spaces) associated with an entity.
-
B.
hasBerths
Indicates that one entity provides or contains sleeping or docking berths for another entity.
-
C.
hasBerthLength
Indicates the length of a berth allocated to or associated with an entity.
-
D.
passengersCountApproximate
Indicates that the number of passengers involved is given as an approximate or estimated count rather than an exact figure.
-
E.
wildCardBerthsCount
Indicates the number of wildcard berths (extra or non-standard qualification spots) allocated in a competition or selection process.
- 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_69e75a8b86c4819089eda22c843b739f |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f474ba127c819086f8f0c698a1bb4d |
completed | May 1, 2026, 9:39 a.m. |
| PD | Predicate disambiguation | batch_69f4683472ec8190a483b3b8afe71720 |
completed | May 1, 2026, 8:45 a.m. |
Created at: April 21, 2026, 12:51 p.m.