Triple
T28425374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shennongjia Mountains |
E720058
|
entity |
| Predicate | forestCoverage |
P71211
|
FINISHED |
| Object | predominantly forested |
—
|
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: predominantly forested | Statement: [Shennongjia Mountains, forestCoverage, predominantly forested]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: forestCoverage Context triple: [Shennongjia Mountains, forestCoverage, predominantly forested]
-
A.
forestCoverCharacteristic
Indicates a relationship where a forested area possesses a specific attribute or quality related to its tree or vegetation cover.
-
B.
forestArea
Indicates the extent or size of land covered by forest within a given area or region.
-
C.
vegetationCoverage
Indicates the extent or proportion of an area that is covered by vegetation.
-
D.
forestDistrict
Indicates that one entity functions as the forest district or forest management administrative unit responsible for the other entity.
-
E.
isForested
chosen
Indicates that an area or region is covered predominantly by forest or dense tree vegetation.
- 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_69eff6f1c5088190bc24bfbf92f9c017 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69f64e37f7fc819083809149b6661e3c |
completed | May 2, 2026, 7:19 p.m. |
| PD | Predicate disambiguation | batch_69f64caede108190a35cc7cbfead866f |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 28, 2026, 1:36 a.m.