Triple
T20693909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sewu Temple |
E508616
|
entity |
| Predicate | distanceToPrambanan |
P141115
|
FINISHED |
| Object | about 800 meters north |
—
|
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 800 meters north | Statement: [Sewu Temple, distanceToPrambanan, about 800 meters north]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToPrambanan Context triple: [Sewu Temple, distanceToPrambanan, about 800 meters north]
-
A.
distanceFromYogyakarta
Indicates the spatial distance between a given place or object and the location of Yogyakarta.
-
B.
distanceFromAngkorWat
Indicates the spatial distance between a given location or entity and Angkor Wat.
-
C.
distanceToPula
Indicates the measured or calculated spatial distance between a given entity and the location Pula.
-
D.
distanceToHampi
Indicates the measured or specified distance between a given entity or location and Hampi.
-
E.
distanceFromKumbakonam
Indicates the spatial distance between a given location and the reference location Kumbakonam.
- 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_69e0b4c1ed408190b72dd26b1e33f8a1 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6c110c4108190a3ab171df33be194 |
completed | April 21, 2026, 12:13 a.m. |
| PD | Predicate disambiguation | batch_69e5c044d1108190b2b5d25de23f6401 |
completed | April 20, 2026, 5:57 a.m. |
| PDg | Predicate description generation | batch_69e5c3caef50819093c8159fe8d6435b |
completed | April 20, 2026, 6:12 a.m. |
Created at: April 16, 2026, 12:09 p.m.