Triple
T35724745
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wat Khun Inthapramun |
E1032576
|
entity |
| Predicate | recliningBuddhaLength |
P14771
|
FINISHED |
| Object | about 50 metres |
—
|
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 50 metres | Statement: [Wat Khun Inthapramun, recliningBuddhaLength, about 50 metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: recliningBuddhaLength Context triple: [Wat Khun Inthapramun, recliningBuddhaLength, about 50 metres]
-
A.
recliningBuddhaLocation
Indicates the location where a reclining Buddha statue or depiction is situated.
-
B.
largestFigureLengthApprox
chosen
Indicates an approximate measurement of the length of the largest figure involved in the relationship or context.
-
C.
hasSculptureLength
Indicates that a sculpture is associated with a specific measurement of its length.
-
D.
templeDimensions
Indicates the measured size or proportions of a temple, such as its length, width, height, or overall spatial layout.
-
E.
reachInInches
Indicates the distance an entity can extend or reach, measured in inches.
- F. None of above.
Provenance (3 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_69f76e102b5881909e5d63a30a5cecbe |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a34f8ee08190a040304635539a8f |
completed | May 3, 2026, 7:34 p.m. |
| PD | Predicate disambiguation | batch_69f7a06f125c8190843af194f042a465 |
completed | May 3, 2026, 7:22 p.m. |
Created at: May 3, 2026, 4:05 p.m.