Triple
T5703123
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fritz (Frankenstein 1931) |
E125717
|
entity |
| Predicate | treatmentOfMonster |
P44049
|
FINISHED |
| Object | torments the monster |
—
|
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: torments the monster | Statement: [Fritz (Frankenstein 1931), treatmentOfMonster, torments the monster]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: treatmentOfMonster Context triple: [Fritz (Frankenstein 1931), treatmentOfMonster, torments the monster]
-
A.
featuresMonster
Indicates that something includes or prominently presents a monster as part of its content or composition.
-
B.
mainMonsterDescription
Indicates the primary descriptive text that characterizes the main monster in a given context or scenario.
-
C.
treatsCharacter
chosen
Indicates how one character behaves toward or interacts with another character, especially in terms of care, respect, or mistreatment.
-
D.
treatment
Indicates that one entity is used as a medical or therapeutic intervention to address, manage, or cure a condition affecting another entity.
-
E.
usesCreature
Indicates that one entity employs, controls, or relies on a creature to perform an action or fulfill a function.
- 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_69c0082c96988190b3a6a201edce472a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02456efb48190bf3aaabcc77cda92 |
completed | March 22, 2026, 5:18 p.m. |
| PD | Predicate disambiguation | batch_69c021c2d8bc8190b947c7d1f423d2f3 |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:45 p.m.