Triple
T5460089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mogao Caves |
E122573
|
entity |
| Predicate | areaOfMurals |
P64194
|
FINISHED |
| Object | over 45,000 square meters |
—
|
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: over 45,000 square meters | Statement: [Mogao Caves, areaOfMurals, over 45,000 square meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaOfMurals Context triple: [Mogao Caves, areaOfMurals, over 45,000 square meters]
-
A.
artistOfMurals
Indicates that an entity creates or is responsible for producing murals.
-
B.
notableMural
Indicates that an entity is a mural distinguished by particular significance, prominence, or recognition.
-
C.
muralsPaintedBetween
Indicates that murals were painted during a specific time interval between two given dates or events.
-
D.
estimatedNumberOfPaintings
Indicates the approximate count of paintings associated with an entity, rather than an exact, verified number.
-
E.
paintedEvery
Indicates that an entity applied paint to each and every relevant item in a specified set or domain.
- 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_69bd46424248819085282ddf50a565f3 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd927c946c8190aef40679199fede3 |
completed | March 20, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_69bd91a0d96c8190bd1299edbf764bbb |
completed | March 20, 2026, 6:27 p.m. |
| PDg | Predicate description generation | batch_69bd927b0b4c81909d5e0f594822e3f9 |
completed | March 20, 2026, 6:31 p.m. |
Created at: March 20, 2026, 2:08 p.m.