Triple
T31266315
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spenser series |
E797260
|
entity |
| Predicate | typicalSettingType |
P207391
|
FINISHED |
| Object | urban setting |
—
|
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: urban setting | Statement: [Spenser series, typicalSettingType, urban setting]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSettingType Context triple: [Spenser series, typicalSettingType, urban setting]
-
A.
typicalSettingConsumed
Indicates the usual context or environment in which something is normally consumed.
-
B.
typicalUnitType
Indicates that one entity is the standard or commonly used unit type associated with measuring or expressing the other entity.
-
C.
typicalRegionType
Indicates that a region is of a characteristic or commonly occurring type for a given context or entity.
-
D.
typicalLotType
Indicates that one entity is the standard or commonly occurring type of lot associated with another entity.
-
E.
typicalPositionType
Indicates the usual or most common positional role or placement type that an entity generally occupies or is associated with.
- F. None of above. chosen
Provenance (4 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_69f224de2bbc819081af6c32e1d857b9 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379e5174c8190a0bdde7e381b7624 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7ee0388190a29faeb5cdb0950a |
completed | May 12, 2026, 7:16 p.m. |
Created at: April 29, 2026, 9:12 p.m.