Triple
T29025614
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donnie Andrews |
E737582
|
entity |
| Predicate | hasRealWorldCounterpartFor |
P86334
|
FINISHED |
| Object | Omar Little |
—
|
NE NERFINISHED |
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: Omar Little | Statement: [Donnie Andrews, hasRealWorldCounterpartFor, Omar Little]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRealWorldCounterpartFor Context triple: [Donnie Andrews, hasRealWorldCounterpartFor, Omar Little]
-
A.
hasRealityCounterpartInFiction
Indicates that a fictional element corresponds to or is based on a real-world counterpart within a work of fiction.
-
B.
characterRealWorldCounterpart
chosen
Indicates that a fictional character is based on, inspired by, or directly corresponds to a specific real-world person.
-
C.
hasCounterpart
Indicates that one entity corresponds to, matches, or serves as an equivalent or parallel version of another entity.
-
D.
hasRealWorldOrigin
Indicates that something is derived from, based on, or directly connected to an actual entity, event, or source in the real world.
-
E.
hasCounterpartName
Indicates that an entity has an alternative or corresponding name used as its counterpart in another context, system, or representation.
- 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_69f077ef00fc81909325f084ad37c035 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69ffab5adf2c819084700c5ea34615bf |
completed | May 9, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69ffaabffa208190b5214ca17cc8a5ea |
completed | May 9, 2026, 9:44 p.m. |
Created at: April 28, 2026, 9:52 a.m.