Triple
T27144089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ryanair Gold Cup |
E681893
|
entity |
| Predicate | approxNumberOfFences |
P49214
|
FINISHED |
| Object | about 16 fences |
—
|
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: about 16 fences | Statement: [Ryanair Gold Cup, approxNumberOfFences, about 16 fences]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approxNumberOfFences Context triple: [Ryanair Gold Cup, approxNumberOfFences, about 16 fences]
-
A.
numberOfFences
chosen
Indicates the quantity of fences associated with or present around a given entity.
-
B.
appearsAsFenceNumber
Indicates that an entity is used or shown as a specific fence identification number in a given context.
-
C.
fenceType
Indicates the specific kind or category of fence associated with or used in relation to an entity.
-
D.
numberOfForts
Indicates the count of forts associated with a given entity or context.
-
E.
numberOfFountains
Indicates the quantitative relationship specifying how many fountains are associated with a given 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_69eefacca3888190b67238d380e8f28b |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f7a225a77c81908f8953ccfeb14336 |
completed | May 3, 2026, 7:29 p.m. |
| PD | Predicate disambiguation | batch_69f7a06d4f108190bae3ab9ae431d2c7 |
completed | May 3, 2026, 7:22 p.m. |
Created at: April 27, 2026, 9:10 a.m.