Triple
T24910416
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Batu Karas Beach |
E623828
|
entity |
| Predicate | approxDistanceFromPangandaran |
P161625
|
FINISHED |
| Object | about 30 kilometers |
—
|
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 30 kilometers | Statement: [Batu Karas Beach, approxDistanceFromPangandaran, about 30 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approxDistanceFromPangandaran Context triple: [Batu Karas Beach, approxDistanceFromPangandaran, about 30 kilometers]
-
A.
distanceFromDenpasar
Indicates the spatial distance between a given location and Denpasar.
-
B.
distanceToPrambanan
Indicates the spatial distance between an entity and the location of Prambanan.
-
C.
distanceToPula
Indicates the measured or calculated spatial distance between a given entity and the location Pula.
-
D.
distanceToMataram
Indicates the measured spatial distance between a given entity’s location and the location of Mataram.
-
E.
distanceFromKundapura
Indicates the measured spatial distance between a given entity and the location of Kundapura.
- 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_69e2fac889c081908e9ff686cb428e5a |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f6200ac60481909895c61d050b1338 |
completed | May 2, 2026, 4:02 p.m. |
| PD | Predicate disambiguation | batch_69f61b37a5648190b10d33ae205ccfee |
completed | May 2, 2026, 3:41 p.m. |
| PDg | Predicate description generation | batch_69f61f109ef48190873bfe18638d2046 |
completed | May 2, 2026, 3:58 p.m. |
Created at: April 18, 2026, 5:27 a.m.