Triple
T21125956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sandymount Park College |
E520557
|
entity |
| Predicate | hasGeographicRegion |
P3227
|
FINISHED |
| Object | Leinster |
—
|
NE NERFINISHED |
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: Leinster | Statement: [Sandymount Park College, hasGeographicRegion, Leinster]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leinster Context triple: [Sandymount Park College, hasGeographicRegion, Leinster]
-
A.
Leinster
chosen
Leinster is a province in eastern Ireland that includes the capital city, Dublin, and is the country’s most populous region.
-
B.
Connacht
Connacht is one of the four traditional provinces of Ireland, located in the west of the island and historically known for its Gaelic culture and rugged landscapes.
-
C.
Munster
Munster is a town in Lower Saxony, Germany, known for its military training areas and location within the Lüneburg Heath region.
-
D.
Munster
Munster is a historic province in the south of Ireland, known for its major role in Irish history, culture, and conflicts, including the 17th-century wars.
-
E.
Munster
Munster is a small town in the Grand Est region of northeastern France, known for its namesake strong-smelling cheese and picturesque setting in the Vosges mountains.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b50a623881909c0bbaf4f2c055e7 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e72238cf708190a4676c40deea2e46 |
completed | April 21, 2026, 7:07 a.m. |
Created at: April 16, 2026, 2:56 p.m.