Triple
T2471020
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Glamis Castle |
E55371
|
entity |
| Predicate | hasGroundsUse |
P7913
|
FINISHED |
| Object | events and weddings |
—
|
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: events and weddings | Statement: [Glamis Castle, hasGroundsUse, events and weddings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGroundsUse Context triple: [Glamis Castle, hasGroundsUse, events and weddings]
-
A.
hasGrounds
Indicates that one entity possesses or includes a physical area of land or outdoor space associated with it.
-
B.
hasHumanUse
Indicates that something is used, employed, or utilized by humans for a particular purpose or benefit.
-
C.
canUse
Indicates that one entity has the ability, permission, or suitability to make use of another entity or resource.
-
D.
coversGrounds
Indicates that one entity extends over, occupies, or lies across the surface area of another entity (typically land or grounds).
-
E.
eligibleUses
chosen
Indicates the types of actions, purposes, or contexts in which something is permitted or qualified to be used.
- 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_69ab49e3622c8190ad22afa2c4fbb807 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd1eb3be481908fa7c6b8f1c78209 |
completed | March 7, 2026, 7:21 a.m. |
| PD | Predicate disambiguation | batch_69abd0b5e3d481909a5cbc4a96edd24f |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:44 p.m.