Triple
T16432430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lewes Priory ruins |
E399099
|
entity |
| Predicate | hasInformationPanels |
P122760
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Lewes Priory ruins, hasInformationPanels, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInformationPanels Context triple: [Lewes Priory ruins, hasInformationPanels, yes]
-
A.
hasPanel
Indicates that one entity includes, is equipped with, or is associated with a panel as a component or feature.
-
B.
hasPanelComposition
Indicates that something is composed of or structured into multiple panels or panel-like sections.
-
C.
hasNumberOfInscribedPanels
Indicates the relationship that specifies how many inscribed panels are associated with a given entity.
-
D.
numberOfPanels
Indicates the total count of distinct panels associated with or contained within a given entity.
-
E.
hasNumberOfVocationalPanels
Indicates the relationship specifying how many vocational panels are associated with a given entity.
- 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_69d87f2b9024819085c20e52de95d583 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32b9f2e8c81909c60b8fb78255e5f |
completed | April 18, 2026, 6:58 a.m. |
| PD | Predicate disambiguation | batch_69e22701d2288190bf8676050758f172 |
completed | April 17, 2026, 12:26 p.m. |
| PDg | Predicate description generation | batch_69e24556c1348190902a4d116c3137d9 |
completed | April 17, 2026, 2:36 p.m. |
Created at: April 10, 2026, 5:10 a.m.