Triple
T20255823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samuel Vanek |
E498697
|
entity |
| Predicate | protectiveMotivation |
P9207
|
FINISHED |
| Object | wants to protect his mother from the Babadook |
—
|
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: wants to protect his mother from the Babadook | Statement: [Samuel Vanek, protectiveMotivation, wants to protect his mother from the Babadook]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: protectiveMotivation Context triple: [Samuel Vanek, protectiveMotivation, wants to protect his mother from the Babadook]
-
A.
perceivedThreat
Indicates that one entity regards another entity or situation as potentially harmful, dangerous, or adverse.
-
B.
protectionMeasures
Indicates actions or safeguards implemented to prevent harm, damage, or risk to someone or something.
-
C.
providesProtectionAgainst
Indicates that one entity serves to guard, shield, or defend another entity from a specified harm, threat, or adverse effect.
-
D.
aimsToProtect
chosen
Indicates an intention or purpose to safeguard or defend one entity, value, or condition from harm, risk, or undesirable outcomes.
-
E.
protects
Indicates taking action to keep someone or something safe from harm, danger, or negative effects.
- 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_69da6275fa6c8190952924930adee150 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e673ab60388190be32cc69bf2b6f76 |
completed | April 20, 2026, 6:42 p.m. |
| PD | Predicate disambiguation | batch_69e55b1b23f88190bdcbe2f81dd226dd |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 11, 2026, 11:41 p.m.