Triple
T37007384
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dungan Mosque |
E915839
|
entity |
| Predicate | regionTypeOfLocation |
P111503
|
FINISHED |
| Object | mountainous region |
—
|
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: mountainous region | Statement: [Dungan Mosque, regionTypeOfLocation, mountainous region]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionTypeOfLocation Context triple: [Dungan Mosque, regionTypeOfLocation, mountainous region]
-
A.
regionTypeOfPlace
chosen
Indicates that a place belongs to or is categorized under a specific type of geographic or administrative region.
-
B.
regionType
Indicates the classification or category of a region, specifying what kind of region it is (e.g., administrative, geographic, or functional).
-
C.
regionalType
Indicates the classification of a region according to its designated type or category within a broader geographic or administrative system.
-
D.
typicalRegionType
Indicates that a region is of a characteristic or commonly occurring type for a given context or entity.
-
E.
urbanDistrictType
Indicates the classification of an urban district according to its specific type or category within an administrative or planning system.
- 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_69f76e90ed548190b187d2475f5c807d |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a0048697bd081909073640666ca6a96 |
completed | May 10, 2026, 8:57 a.m. |
| PD | Predicate disambiguation | batch_6a0047bf3c248190a9ac97a7afdfe2cb |
completed | May 10, 2026, 8:54 a.m. |
Created at: May 3, 2026, 4:14 p.m.