Triple
T20447325
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aughnacloy |
E501550
|
entity |
| Predicate | distanceToDungannon_km |
P140147
|
FINISHED |
| Object | approximately 24 |
—
|
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: approximately 24 | Statement: [Aughnacloy, distanceToDungannon_km, approximately 24]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToDungannon_km Context triple: [Aughnacloy, distanceToDungannon_km, approximately 24]
-
A.
distanceFromEnniskillen
Indicates the spatial distance between a given entity and the location of Enniskillen.
-
B.
distanceFromDublinCityCentre_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the center of Dublin city.
-
C.
distanceToBelfast
Indicates the spatial distance between a given entity’s location and the city of Belfast.
-
D.
distanceFromCorkCity
Indicates the spatial distance between a given place and Cork City.
-
E.
distanceToColeraine
Indicates the spatial distance between a given location or entity and the place named Coleraine.
- 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_69e0b4ac0a1c81908845d0f8a56abce8 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e68cfe57a8819094bd3d324bd567f5 |
completed | April 20, 2026, 8:30 p.m. |
| PD | Predicate disambiguation | batch_69e57679eb40819086142df3e39c928e |
completed | April 20, 2026, 12:42 a.m. |
| PDg | Predicate description generation | batch_69e58d766b408190a1d3698145fb6d30 |
completed | April 20, 2026, 2:20 a.m. |
Created at: April 16, 2026, 11:32 a.m.