Triple
T26642063
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dangqiong Co |
E668805
|
entity |
| Predicate | relevantField |
P3
|
FINISHED |
| Object | Tibetan Plateau environmental studies |
—
|
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: Tibetan Plateau environmental studies | Statement: [Dangqiong Co, relevantField, Tibetan Plateau environmental studies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relevantField Context triple: [Dangqiong Co, relevantField, Tibetan Plateau environmental studies]
-
A.
relatedField
Indicates that one field, topic, or area of study is connected or relevant to another in subject matter or application.
-
B.
relatedWorkField
Indicates that one work is associated with or pertains to the same or a relevant field, discipline, or area of activity as another work.
-
C.
eligibleWorkField
Indicates that a particular field of work is considered suitable or permitted for a given entity (such as a person, role, or position).
-
D.
fieldOfSignificance
Indicates that something holds particular importance, relevance, or impact within a specified domain, context, or area of interest.
-
E.
fieldOfWork
chosen
Indicates the professional or academic domain in which an entity is primarily engaged or specializes.
- 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_69ee9d0024b8819090a7c8cf669a3b6c |
completed | April 26, 2026, 11:17 p.m. |
| NER | Named-entity recognition | batch_69f61fd623bc819091df736cf3419b99 |
completed | May 2, 2026, 4:01 p.m. |
| PD | Predicate disambiguation | batch_69f61b3d23f481908dfec27adace900a |
completed | May 2, 2026, 3:41 p.m. |
Created at: April 27, 2026, 2:29 a.m.