Triple
T16575615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liverpool Cruise Terminal |
E402700
|
entity |
| Predicate | hasTerminalBuildingArea |
P24212
|
FINISHED |
| Object | approx 1,000 square 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: approx 1,000 square metres | Statement: [Liverpool Cruise Terminal, hasTerminalBuildingArea, approx 1,000 square metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTerminalBuildingArea Context triple: [Liverpool Cruise Terminal, hasTerminalBuildingArea, approx 1,000 square metres]
-
A.
hasFloorArea
chosen
Indicates that an entity possesses a specified amount of floor space as a measurable area.
-
B.
hasTerminalBuildings
Indicates that one entity possesses or includes terminal buildings associated with it.
-
C.
hasBaseBuildingFloors
Indicates that something (such as a building or structure) has a specified number of floors in its base or main part.
-
D.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
E.
hasBaseArea
Indicates that one entity has a base whose surface area is quantified or associated with another entity.
- 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_69d88387363c8190a97a0c942130de97 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3595cb65481909be62a52deff3d44 |
completed | April 18, 2026, 10:13 a.m. |
| PD | Predicate disambiguation | batch_69e296a7d9d0819088555bca6c936e79 |
completed | April 17, 2026, 8:23 p.m. |
Created at: April 10, 2026, 5:16 a.m.