Triple
T10961285
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hennessy Gold Cup |
E258978
|
entity |
| Predicate | fenceCount |
P49214
|
FINISHED |
| Object | 21 fences (approximate) |
—
|
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: 21 fences (approximate) | Statement: [Hennessy Gold Cup, fenceCount, 21 fences (approximate)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fenceCount Context triple: [Hennessy Gold Cup, fenceCount, 21 fences (approximate)]
-
A.
numberOfFences
chosen
Indicates the quantity of fences associated with or present around a given entity.
-
B.
fences
Indicates that one entity constructs, installs, or maintains a fence or barrier around or between entities or areas.
-
C.
hasBorderWallOrFence
Indicates that a physical barrier such as a wall or fence exists along the border between two entities.
-
D.
numberOfColumnsOnFlanks
Indicates the count of columns located on the flanking sides of a structure or object.
-
E.
roofCount
Indicates the number of distinct roofs associated with an entity (such as a building or structure).
- 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_69d6aa88500c819097d7032ca578e74f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7712bf65c8190b847784d885876fd |
completed | April 9, 2026, 9:28 a.m. |
| PD | Predicate disambiguation | batch_69d72e874f48819096ffa878f90c7d5b |
completed | April 9, 2026, 4:43 a.m. |
Created at: April 8, 2026, 9:23 p.m.