Triple
T9775383
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bay of Islands |
E237232
|
entity |
| Predicate | distanceFromAuckland_km |
P90579
|
FINISHED |
| Object | about 230 |
—
|
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 230 | Statement: [Bay of Islands, distanceFromAuckland_km, about 230]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromAuckland_km Context triple: [Bay of Islands, distanceFromAuckland_km, about 230]
-
A.
distanceFromNewZealandMainland_km
Indicates the distance, measured in kilometers, between an entity’s location and the mainland of New Zealand.
-
B.
distanceToWellington
Indicates the measured distance between a given entity’s location and the location of Wellington.
-
C.
distanceToPalmerstonNorth
Indicates the spatial distance between a given location and Palmerston North.
-
D.
distanceToChristchurch
Indicates the spatial distance between a given entity’s location and the location of Christchurch.
-
E.
distanceFromSydney
Indicates the spatial distance between a given location and the city of Sydney.
- 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_69ca84d975a08190aab25b02a89bdab3 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda13148288190bcbb3b4a066d9fc1 |
completed | April 1, 2026, 10:50 p.m. |
| PD | Predicate disambiguation | batch_69cd03d3b68c81909e570401a891b9f2 |
completed | April 1, 2026, 11:38 a.m. |
| PDg | Predicate description generation | batch_69cd06aa8bc88190904be19c8953def8 |
completed | April 1, 2026, 11:51 a.m. |
Created at: March 30, 2026, 8:26 p.m.