Triple
T13105111
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Angeles |
E310823
|
entity |
| Predicate | hasSecurityMeasureInFiction |
P49797
|
FINISHED |
| Object | automated law enforcement systems |
—
|
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: automated law enforcement systems | Statement: [San Angeles, hasSecurityMeasureInFiction, automated law enforcement systems]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSecurityMeasureInFiction Context triple: [San Angeles, hasSecurityMeasureInFiction, automated law enforcement systems]
-
A.
guardedByInFiction
Indicates that one fictional entity is protected or watched over by another within a narrative context.
-
B.
hasRiskInFiction
Indicates that a subject is associated with a potential danger, threat, or harmful outcome within a fictional or narrative context.
-
C.
protectionMeasures
chosen
Indicates actions or safeguards implemented to prevent harm, damage, or risk to someone or something.
-
D.
fictionalSecurityLevel
Indicates the degree or category of security status assigned within a fictional or imagined context.
-
E.
hasSecurityNotion
Indicates that one entity possesses, defines, or is associated with a particular concept or notion of security in relation to another entity 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_69d806a872d08190a329806f8ff30df4 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98154c9f48190aeca779d97151759 |
completed | April 10, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69d98041a3548190a05ddd83dbb660fa |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9:05 p.m.