Triple
T31265162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sagaing Hills |
E797233
|
entity |
| Predicate | hasMonasteryCountApprox |
P195050
|
FINISHED |
| Object | hundreds of monasteries |
—
|
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: hundreds of monasteries | Statement: [Sagaing Hills, hasMonasteryCountApprox, hundreds of monasteries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMonasteryCountApprox Context triple: [Sagaing Hills, hasMonasteryCountApprox, hundreds of monasteries]
-
A.
hasMonasteryDedicatedTo
Indicates that a place or institution possesses a monastery whose religious dedication is to a specific figure, deity, or sacred concept.
-
B.
containsMonastery
Indicates that one entity includes or encompasses a monastery within its boundaries or composition.
-
C.
hasNearbyMonastery
Indicates that one entity is located close to or in the vicinity of a monastery.
-
D.
numberOfMonksApprox
Indicates an approximate count or estimate of how many monks are involved or present in a given context.
-
E.
hasMonksResidence
Indicates that a place includes or is associated with a residence where monks live.
- 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_69f224de2bbc819081af6c32e1d857b9 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fd9ff026a48190bfec33deeb3b2c43 |
completed | May 8, 2026, 8:33 a.m. |
| PD | Predicate disambiguation | batch_69fd97d805bc8190ba12f429d3ad04c7 |
completed | May 8, 2026, 7:59 a.m. |
| PDg | Predicate description generation | batch_69fd9fef7aac819089cc88dd3d00296d |
completed | May 8, 2026, 8:33 a.m. |
Created at: April 29, 2026, 9:12 p.m.