Triple
T20150372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Warner Village |
E491418
|
entity |
| Predicate | typicalSettingDepicted |
P94330
|
FINISHED |
| Object | American suburb |
—
|
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: American suburb | Statement: [Warner Village, typicalSettingDepicted, American suburb]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSettingDepicted Context triple: [Warner Village, typicalSettingDepicted, American suburb]
-
A.
portrayedInSetting
Indicates that an entity is depicted or represented within a particular setting, environment, or context.
-
B.
typicalSettingConsumed
Indicates the usual context or environment in which something is normally consumed.
-
C.
placeOfSetting
Indicates the location or environment where an event, scene, or situation takes place.
-
D.
typicallyDepicts
chosen
Indicates that one entity is most commonly or characteristically portrayed or represented by the other in depictions or images.
-
E.
hasFictionalSettingElement
Indicates that something includes or is associated with a specific element or component of a fictional setting.
- 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_69da6265f8f0819080b29c752a574088 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e667a1c5848190975b17ab07251f8b |
completed | April 20, 2026, 5:51 p.m. |
| PD | Predicate disambiguation | batch_69e54cfd924881909b55f3e4d3e7e070 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 11:33 p.m.