Triple
T12575336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | วัดอรุณ |
E300190
|
entity |
| Predicate | มีพระอุโบสถ |
P105503
|
FINISHED |
| Object | ประดิษฐานพระพุทธรูปสำคัญ |
—
|
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: ประดิษฐานพระพุทธรูปสำคัญ | Statement: [วัดอรุณ, มีพระอุโบสถ, ประดิษฐานพระพุทธรูปสำคัญ]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: มีพระอุโบสถ Context triple: [วัดอรุณ, มีพระอุโบสถ, ประดิษฐานพระพุทธรูปสำคัญ]
-
A.
hasCathedralDiocese
Indicates that a cathedral is associated with or belongs to a particular diocese within a church’s administrative structure.
-
B.
containsCathedral
Indicates that one entity includes or has within its boundaries a cathedral associated with it.
-
C.
hasBaptistery
Indicates that an entity includes, contains, or is associated with a baptistery as part of its structure or facilities.
-
D.
hasProCathedral
Indicates that one entity serves as or possesses a pro-cathedral (a church temporarily acting as a cathedral) in relation to another entity.
-
E.
hasSacristy
Indicates that a religious building includes or is associated with a sacristy (a room where sacred vessels, vestments, and liturgical items are kept and prepared).
- 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_69d7bde87b648190bcd0266e9efde098 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9550d84908190aea0f50055f6d92e |
completed | April 10, 2026, 7:52 p.m. |
| PD | Predicate disambiguation | batch_69d95414692881909c52a1de7d224b44 |
completed | April 10, 2026, 7:48 p.m. |
| PDg | Predicate description generation | batch_69d9550af6d48190a40e349ed0424be3 |
completed | April 10, 2026, 7:52 p.m. |
Created at: April 9, 2026, 4:46 p.m.