Triple
T34495534
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kai Tak Cruise Terminal |
E885595
|
entity |
| Predicate | maximumBerthLength |
P123373
|
FINISHED |
| Object | about 850 metres |
—
|
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: about 850 metres | Statement: [Kai Tak Cruise Terminal, maximumBerthLength, about 850 metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumBerthLength Context triple: [Kai Tak Cruise Terminal, maximumBerthLength, about 850 metres]
-
A.
hasBerthLength
chosen
Indicates the length of a berth allocated to or associated with an entity.
-
B.
numberOfBerths
Indicates the quantity of berths (sleeping places or docking spaces) associated with an entity.
-
C.
maximumVesselLength
Indicates the greatest allowable or observed length of a vessel in a given context or constraint.
-
D.
maximumBoatBeamInMetres
Indicates the maximum width of a boat, measured in metres, that is allowed or applicable in the given context.
-
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_69f349cafcec8190997b45b3fdc16c27 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fcf825ca7081909d06b0df33eb33f9 |
completed | May 7, 2026, 8:37 p.m. |
| PD | Predicate disambiguation | batch_69fcf42160f0819096812a8bf590875e |
completed | May 7, 2026, 8:20 p.m. |
Created at: May 1, 2026, 2:01 a.m.