Triple
T28373809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 武当山 |
E718701
|
entity |
| Predicate | numberOfAncientBuildings |
P198772
|
FINISHED |
| Object | 数百座 |
—
|
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: 数百座 | Statement: [武当山, numberOfAncientBuildings, 数百座]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAncientBuildings Context triple: [武当山, numberOfAncientBuildings, 数百座]
-
A.
numberOfReconstructedHistoricalBuildings
Indicates the count of historical buildings that have been rebuilt or restored to a previous state.
-
B.
numberOfArchaeologicalSites
Indicates the total count of archaeological sites associated with a given entity or context.
-
C.
usesAncientStructure
Indicates that one entity makes use of, incorporates, or relies on an ancient structure in its function, design, or activity.
-
D.
numberOfNewTraditionalStyleBuildings
Indicates the count of newly constructed buildings that follow a traditional architectural style.
-
E.
hasNumberOfHistoricBuildings
chosen
Indicates the quantity of historic buildings associated with a given entity.
- 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_69eff6ee5afc8190bd7375a29f0cc6c6 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69ff6a4ce9a08190b98abde3a170dd69 |
completed | May 9, 2026, 5:09 p.m. |
| PD | Predicate disambiguation | batch_69ff69c11634819089d1084bd2c11534 |
completed | May 9, 2026, 5:07 p.m. |
Created at: April 28, 2026, 1:01 a.m.