Triple
T2246404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Count László de Almásy |
E49513
|
entity |
| Predicate | characterArcTheme |
P30025
|
FINISHED |
| Object | memory and identity |
—
|
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: memory and identity | Statement: [Count László de Almásy, characterArcTheme, memory and identity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterArcTheme Context triple: [Count László de Almásy, characterArcTheme, memory and identity]
-
A.
characterArc
Indicates the developmental journey or transformation a character undergoes over the course of a narrative.
-
B.
themeInvolvingCharacter
chosen
Indicates that a theme, motif, or abstract concept centrally involves or is significantly shaped by a particular character.
-
C.
protagonistCharacteristic
Indicates that a characteristic, trait, or defining quality is attributed to the protagonist in a narrative or scenario.
-
D.
characterAlignment
Indicates the moral or ethical stance a character holds, typically along axes such as good–evil and lawful–chaotic.
-
E.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
- 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_69abc0ec46b08190ae12de8b255eb71d |
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.