Triple
T2246375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Count László de Almásy |
E49513
|
entity |
| Predicate | memoryCondition |
P9896
|
FINISHED |
| Object | fragmented memories |
—
|
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: fragmented memories | Statement: [Count László de Almásy, memoryCondition, fragmented memories]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: memoryCondition Context triple: [Count László de Almásy, memoryCondition, fragmented memories]
-
A.
memory
Indicates that an entity retains, recalls, or is associated with stored information or past experiences.
-
B.
memoryType
chosen
Indicates the specific category or kind of memory associated with an entity or process.
-
C.
memorizedBy
Indicates that some content, information, or material has been learned and retained in memory by a particular entity.
-
D.
rememberedFor
Indicates that one entity is known or recognized primarily because of, or in association with, another entity or achievement.
-
E.
memoryModel
Indicates a relationship where an entity serves as or uses a specific model or framework for representing, organizing, or managing memory.
- 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_69a88aa979788190ad6500f1d8eee2fc |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc0ea75d881909d4e176a432f32e8 |
completed | March 7, 2026, 6:08 a.m. |
| PD | Predicate disambiguation | batch_69abbdb160248190aa75b38f11ad8602 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:47 p.m.