Triple
T1336172
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port of Kobe |
E28753
|
entity |
| Predicate | hasBerths |
P27883
|
FINISHED |
| Object | multiple deep-water berths |
—
|
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: multiple deep-water berths | Statement: [Port of Kobe, hasBerths, multiple deep-water berths]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBerths Context triple: [Port of Kobe, hasBerths, multiple deep-water berths]
-
A.
hasHarbor
Indicates that a place possesses or contains a harbor for docking or sheltering vessels.
-
B.
hasPier
Indicates that a location or structure possesses or includes a pier as part of its features.
-
C.
hasFerryPort
Indicates that a place serves as a location where ferries regularly dock to load and unload passengers or cargo.
-
D.
wildCardBerthsCount
Indicates the number of wildcard berths (extra or non-standard qualification spots) allocated in a competition or selection process.
-
E.
hasSeaAccess
Indicates that an entity has direct access to the sea, typically via a coastline, port, or navigable waterway connected to the sea.
- 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_69a498561a508190a3e1bc137c2b866a |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c1ecb5208190a9eadda113c91e66 |
completed | March 1, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69a4bef174708190a07bbc697fe19a2d |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c1bf31988190a659f48fe018f4bc |
completed | March 1, 2026, 10:46 p.m. |
Created at: March 1, 2026, 7:55 p.m.