Triple
T8051607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London Gateway Port |
E187685
|
entity |
| Predicate | hasCraneType |
P80750
|
FINISHED |
| Object | ship-to-shore gantry cranes |
—
|
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: ship-to-shore gantry cranes | Statement: [London Gateway Port, hasCraneType, ship-to-shore gantry cranes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCraneType Context triple: [London Gateway Port, hasCraneType, ship-to-shore gantry cranes]
-
A.
hasCraneCapacity
Indicates that an entity possesses a crane with a specified lifting capacity or load-handling capability.
-
B.
hasLiftType
Indicates the specific type or category of lift associated with an entity.
-
C.
hasCabType
Indicates that an entity is associated with or characterized by a specific type or category of cab.
-
D.
hasUndercarriageType
Indicates the specific type or configuration of undercarriage that an object (typically a vehicle or machine) possesses.
-
E.
hasTankType
Indicates that an entity is associated with, or classified by, a specific type or category of tank.
- 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_69ca82b15e948190a62fd7af5218426a |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3f7aab6481909d4a7cb9eabcd2af |
completed | March 31, 2026, 3:28 a.m. |
| PD | Predicate disambiguation | batch_69cb049a1b9c8190811c396421ebf9c9 |
completed | March 30, 2026, 11:17 p.m. |
| PDg | Predicate description generation | batch_69cb14bcbbc0819094a98e7ffffb7a40 |
completed | March 31, 2026, 12:26 a.m. |
Created at: March 30, 2026, 5:24 p.m.