Triple
T30768255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Serene |
E783436
|
entity |
| Predicate | isRelatedToWord |
P37
|
FINISHED |
| Object | serene |
—
|
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: serene | Statement: [Serene, isRelatedToWord, serene]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isRelatedToWord Context triple: [Serene, isRelatedToWord, serene]
-
A.
linguisticallyRelatedTo
Indicates that two entities are connected through a linguistic relationship, such as sharing a common language, origin, structure, or other language-based association.
-
B.
relatedToTerm
Indicates a general, non-specific relationship or association between one term and another.
-
C.
semanticRelation
Indicates a general meaning-based connection between two entities, such as similarity, implication, or conceptual association.
-
D.
relatedTo
chosen
Indicates a general, non-specific relationship or association exists between two entities.
-
E.
moreCloselyRelatedTo
Indicates that one entity has a stronger or closer relationship, connection, or similarity to a second entity than to some other reference 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_69f224b1519081908b9db003fd2073e0 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f68fbffc3481909fc9b762f2cc5dd4 |
completed | May 2, 2026, 11:58 p.m. |
| PD | Predicate disambiguation | batch_69f68b7b03488190b1db5fde4c7dd6e5 |
completed | May 2, 2026, 11:40 p.m. |
Created at: April 29, 2026, 8:40 p.m.