Triple
T10354552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Swankie |
E243962
|
entity |
| Predicate | basedOnRealLife |
P93832
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Swankie, basedOnRealLife, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedOnRealLife Context triple: [Swankie, basedOnRealLife, yes]
-
A.
basedOnInFiction
Indicates that a fictional work, character, or element is derived from, inspired by, or modeled after another real or fictional source.
-
B.
hasFictionalTownBasedOn
Indicates that a fictional town is modeled on, inspired by, or derived from a specific real-world town or location.
-
C.
depictionBasedOn
Indicates that one depiction is created using another work, image, or representation as its source or reference.
-
D.
basedOnFictionalDateIn
Indicates that something is derived from, inspired by, or determined using a fictional date occurring within a specified work, timeline, or fictional context.
-
E.
basedOnRealPersonFor
Indicates that one entity is created, modeled, or inspired using a specific real person as its basis.
- 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_69d381b22b8c8190aaed476be5f872a9 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e952c878819084e5d7a593a3f9e9 |
completed | April 7, 2026, 11:24 a.m. |
| PD | Predicate disambiguation | batch_69d4dfa657f481909cc5cc8fec00ad19 |
completed | April 7, 2026, 10:42 a.m. |
| PDg | Predicate description generation | batch_69d4e91ce2008190af252c140370b7f2 |
completed | April 7, 2026, 11:23 a.m. |
Created at: April 6, 2026, 11:58 a.m.