Triple
T30956619
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beijing 2008 Olympic Torch Relay |
E788693
|
entity |
| Predicate | torchMotif |
P41002
|
FINISHED |
| Object | cloud patterns |
—
|
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: cloud patterns | Statement: [Beijing 2008 Olympic Torch Relay, torchMotif, cloud patterns]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: torchMotif Context triple: [Beijing 2008 Olympic Torch Relay, torchMotif, cloud patterns]
-
A.
transformationMotif
Indicates a recurring pattern or theme in which one entity undergoes a change in form, state, or identity in relation to another.
-
B.
featuresMotif
chosen
Indicates that something contains, incorporates, or prominently includes a particular recurring motif or pattern.
-
C.
bindsSequenceMotif
Indicates that one entity specifically recognizes and attaches to a particular sequence motif of another entity.
-
D.
usesMotifsFrom
Indicates that one entity incorporates or draws upon recurring themes, patterns, or elements that originate from another entity.
-
E.
learningModel
Indicates that one entity functions as a learning model used to learn from data or examples in relation to another 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_69f224c28c1881908c33b45d689f1724 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6953bafb88190a860e9c68a3dd4b2 |
completed | May 3, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69f690ef92308190903a54fc74233269 |
completed | May 3, 2026, 12:03 a.m. |
Created at: April 29, 2026, 8:54 p.m.