Triple
T299509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frauenliebe und -leben |
E6166
|
entity |
| Predicate | cycleCharacter |
P10582
|
FINISHED |
| Object | intimate |
—
|
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: intimate | Statement: [Frauenliebe und -leben, cycleCharacter, intimate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cycleCharacter Context triple: [Frauenliebe und -leben, cycleCharacter, intimate]
-
A.
characterBasedOn
Indicates that one character is modeled, inspired, or derived from another real or fictional entity.
-
B.
characterRoleSwap
Indicates a relationship where two characters exchange or assume each other’s narrative roles or functions within a story or scenario.
-
C.
followsCycle
Indicates that one entity adheres to or repeats a recurring sequence, pattern, or cycle defined or exemplified by another entity.
-
D.
supportingCharacter
Indicates that one entity plays a secondary or assisting role in the story or context relative to another primary entity.
-
E.
zoningCharacter
Indicates how the regulatory or functional nature of a geographic area is defined or classified in terms of land-use zoning.
- 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_69a2e79114b081909490b3bf5a5dbb51 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea0dd1dc8190aecd5afdeb2fd74b |
completed | Feb. 28, 2026, 1:13 p.m. |
| PD | Predicate disambiguation | batch_69a2e9398df08190af40063a2de7a1d0 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea07e3bc8190bae593b3264de211 |
completed | Feb. 28, 2026, 1:13 p.m. |
Created at: Feb. 28, 2026, 1:06 p.m.