Triple
T23882942
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wallendbeen |
E600253
|
entity |
| Predicate | distanceToHarden_km |
P153931
|
FINISHED |
| Object | approximately 25 |
—
|
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 25 | Statement: [Wallendbeen, distanceToHarden_km, approximately 25]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToHarden_km Context triple: [Wallendbeen, distanceToHarden_km, approximately 25]
-
A.
lengthInKm
Indicates that one entity specifies the length or distance of another entity measured in kilometers.
-
B.
hardness
Indicates the degree to which one entity resists being scratched, indented, or deformed by another.
-
C.
distanceFromNearestSettlementKilometers
Indicates the distance, measured in kilometers, from an entity’s location to the closest human settlement.
-
D.
distanceToHayByRoad_km
Indicates the distance, measured in kilometers, between two locations when traveling by road.
-
E.
distanceFromHanaTown (miles)
Indicates the number of miles separating a given place or entity from Hana Town.
- 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_69e295318e148190b9979d8fc02e168f |
completed | April 17, 2026, 8:16 p.m. |
| NER | Named-entity recognition | batch_69f1ccfaef348190b4820b6f3648c60c |
completed | April 29, 2026, 9:18 a.m. |
| PD | Predicate disambiguation | batch_69f1614e24b48190a1c8fb5b7c75ee0f |
completed | April 29, 2026, 1:39 a.m. |
| PDg | Predicate description generation | batch_69f167dca3608190ace9d2eef56b2af6 |
completed | April 29, 2026, 2:07 a.m. |
Created at: April 17, 2026, 8:24 p.m.