Triple
T16538401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waharoa |
E401753
|
entity |
| Predicate | distanceToMatamata_km |
P123950
|
FINISHED |
| Object | approximately 7 |
—
|
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 7 | Statement: [Waharoa, distanceToMatamata_km, approximately 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToMatamata_km Context triple: [Waharoa, distanceToMatamata_km, approximately 7]
-
A.
distanceToBega_km
Indicates the physical distance, measured in kilometers, between a given location and Bega.
-
B.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
C.
distanceToMoshi
Indicates the spatial distance between a given entity and the location Moshi.
-
D.
distanceToProvinceCapital_km
Indicates the distance, measured in kilometers, between a given location and the capital city of its province.
-
E.
distanceToMetzKilometers
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Metz.
- 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_69d88384bc30819084229e7dcdc39a41 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3455a98b481909e489ea7bd01570a |
completed | April 18, 2026, 8:48 a.m. |
| PD | Predicate disambiguation | batch_69e2969fab208190ad64164d24748c45 |
completed | April 17, 2026, 8:22 p.m. |
| PDg | Predicate description generation | batch_69e2d7f97e548190a474691a152bd8e8 |
completed | April 18, 2026, 1:01 a.m. |
Created at: April 10, 2026, 5:15 a.m.