Triple
T753761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crumlin Road Gaol |
E15506
|
entity |
| Predicate | numberOfExecutions |
P19112
|
FINISHED |
| Object | 17 |
—
|
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: 17 | Statement: [Crumlin Road Gaol, numberOfExecutions, 17]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfExecutions Context triple: [Crumlin Road Gaol, numberOfExecutions, 17]
-
A.
numberOfInstances
Indicates the quantity or count of distinct occurrences or instances associated with a given entity or context.
-
B.
numberOfTests
Indicates the quantity of tests associated with or performed in a given context or entity.
-
C.
numberOfEvents
Indicates the quantity or count of events associated with a given entity or context.
-
D.
numberOfCases
Indicates the total count of individual instances, occurrences, or records associated with a particular situation, condition, or category.
-
E.
plannedNumberOfTests
Indicates the total count of tests that are intended or scheduled to be conducted for a given context or period.
- 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_69a493599a0081908da65f3407af1ef2 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a64ecadc8190a82e25444e7abba6 |
completed | March 1, 2026, 8:49 p.m. |
| PD | Predicate disambiguation | batch_69a4a501c4cc81908de6d63e3d4f60d7 |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a5bed20c81909ecc28bf42594e72 |
completed | March 1, 2026, 8:46 p.m. |
Created at: March 1, 2026, 7:37 p.m.