Triple
T13105115
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Angeles |
E310823
|
entity |
| Predicate | hasPublicImageInFiction |
P106216
|
FINISHED |
| Object | crime-free city |
—
|
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: crime-free city | Statement: [San Angeles, hasPublicImageInFiction, crime-free city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPublicImageInFiction Context triple: [San Angeles, hasPublicImageInFiction, crime-free city]
-
A.
hasPlaceInFiction
Indicates that a fictional work or element is associated with, set in, or takes place within a particular fictional location or setting.
-
B.
hasAssociatedWorkOfFiction
Indicates that an entity is linked to a related work of fiction, such as a novel, film, or story that is associated with it.
-
C.
hasRelativeInFiction
Indicates that one entity has a relative or family member who appears as a character within a fictional work associated with the other entity.
-
D.
hasFeatureInFiction
chosen
Indicates that a fictional work includes or portrays a particular feature, trait, or characteristic.
-
E.
hasChildInFiction
Indicates that a fictional work or character includes another character as their child within the fictional narrative.
- 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.