Triple
T874361
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna Karenina |
E18882
|
entity |
| Predicate | hasTragicEnding |
P21136
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Anna Karenina, hasTragicEnding, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTragicEnding Context triple: [Anna Karenina, hasTragicEnding, true]
-
A.
hasTragicPast
Indicates that an entity has experienced a significantly sorrowful or traumatic history that influences its present state or characterization.
-
B.
endedWith
Indicates that one event, process, or state concluded with or was finalized by another specified event, condition, or outcome.
-
C.
hasConditionalEnding
Indicates that one entity concludes or terminates only if a specified condition involving another entity is met.
-
D.
hasDiedInSeries
Indicates that an entity has died at some point within the narrative of a particular series.
-
E.
includesSpokenEpilogue
Indicates that the work contains a spoken epilogue segment delivered after the main content concludes.
- 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_69a4938db1f081909bcd1ad2713b6096 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac992d8c819088800f5a713fa7a4 |
completed | March 1, 2026, 9:16 p.m. |
| PD | Predicate disambiguation | batch_69a4aa8b9b5c81909ac71904f8b8b5cd |
completed | March 1, 2026, 9:07 p.m. |
| PDg | Predicate description generation | batch_69a4abb157d08190a7d7281eb3f1b788 |
completed | March 1, 2026, 9:12 p.m. |
Created at: March 1, 2026, 7:39 p.m.