Triple
T504070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manchester Central Convention Complex |
E10462
|
entity |
| Predicate | hasMainHallSpan |
P4063
|
FINISHED |
| Object | 64 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: 64 metres | Statement: [Manchester Central Convention Complex, hasMainHallSpan, 64 metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMainHallSpan Context triple: [Manchester Central Convention Complex, hasMainHallSpan, 64 metres]
-
A.
hasMainHall
Indicates that an entity possesses or includes a primary or central hall as a significant internal space.
-
B.
hasMainSpanLength
chosen
Indicates the relationship specifying the primary or main span’s length associated with an entity.
-
C.
numberOfMainCourtyards
Indicates the quantity of primary courtyards associated with an entity.
-
D.
hasCorridor
Indicates that one entity includes, is connected by, or provides access through a corridor to another entity.
-
E.
numberOfHalls
Indicates the quantity of halls associated with a given entity or location.
- 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_69a2e848adf881908e5e04f7af030093 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f149bd1c81908ff58ac504ace2bf |
completed | Feb. 28, 2026, 1:44 p.m. |
| PD | Predicate disambiguation | batch_69a2edfbb7e0819092cf29c2c68fe8fb |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.