Triple
T5982670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EAT/DIE |
E133154
|
entity |
| Predicate | intendedInterpretation |
P67724
|
FINISHED |
| Object | reflection on everyday acts and ultimate fate |
—
|
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: reflection on everyday acts and ultimate fate | Statement: [EAT/DIE, intendedInterpretation, reflection on everyday acts and ultimate fate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: intendedInterpretation Context triple: [EAT/DIE, intendedInterpretation, reflection on everyday acts and ultimate fate]
-
A.
containsInterpretationOf
Indicates that one entity includes or embodies an interpretation or understanding of another entity.
-
B.
primaryInterpretation
Indicates that one interpretation of an entity, expression, or signal is designated as its main or most significant meaning among possible alternatives.
-
C.
interpretedAs
Indicates that something is understood, perceived, or taken to mean something else, often based on context or subjective judgment.
-
D.
interpretationMethod
Indicates the method, technique, or process used to interpret or derive meaning from something.
-
E.
interpretedInCase
Indicates that something is understood, analyzed, or given meaning within the context of a particular case or specific situational scenario.
- 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_69c0086f45e8819098f73dd16d45ec9d |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04a6a9f2c8190b900cd7e3ab9fe42 |
completed | March 22, 2026, 8 p.m. |
| PD | Predicate disambiguation | batch_69c049de98648190962b14fd341c93da |
completed | March 22, 2026, 7:58 p.m. |
| PDg | Predicate description generation | batch_69c04a663a4481908983048c69cba6b6 |
completed | March 22, 2026, 8 p.m. |
Created at: March 22, 2026, 4:04 p.m.