Triple
T18204384
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | T5 |
E435867
|
entity |
| Predicate | treatsEveryNLPTasAs |
P130211
|
FINISHED |
| Object | text-to-text problem |
—
|
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: text-to-text problem | Statement: [T5, treatsEveryNLPTasAs, text-to-text problem]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: treatsEveryNLPTasAs Context triple: [T5, treatsEveryNLPTasAs, text-to-text problem]
-
A.
treatsCaseAs
Indicates that one entity handles, regards, or processes another entity specifically as a case or instance within a particular context or framework.
-
B.
treatsRightAs
Indicates that one entity provides medical or therapeutic treatment to another entity who is identified as the right-hand participant in the relationship.
-
C.
treatsLexicalItemsAs
Indicates that one entity regards or handles certain lexical items in a particular way or according to a specific role, function, or interpretation.
-
D.
treatsSyllogismAs
Indicates that one entity regards, interprets, or handles something in the manner or role of a syllogism.
-
E.
treatsHumansAs
Indicates how one entity regards or behaves toward humans, characterizing them in a particular way (e.g., as equals, tools, resources, or threats).
- F. None of above. chosen
Provenance (4 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_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e222831081908f7d5500424e3acb |
completed | April 19, 2026, 2:09 p.m. |
| PD | Predicate disambiguation | batch_69e4332155d88190b106d0dceb4554af |
completed | April 19, 2026, 1:42 a.m. |
| PDg | Predicate description generation | batch_69e438f684e48190b38c64b58c518b6a |
completed | April 19, 2026, 2:07 a.m. |
Created at: April 10, 2026, 10:32 a.m.