Triple
T21077079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bonnie and Clyde death car exhibit |
E519263
|
entity |
| Predicate | usesOriginalObject |
P142742
|
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: [Bonnie and Clyde death car exhibit, usesOriginalObject, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesOriginalObject Context triple: [Bonnie and Clyde death car exhibit, usesOriginalObject, yes]
-
A.
hasOriginalVersion
Indicates that one entity is the original or initial version from which another entity is derived or adapted.
-
B.
hasExplicitOriginal
Indicates that something has a clearly specified, directly stated original source or version from which it is derived.
-
C.
hasOriginalNameOf
Indicates that one entity is the original or earlier name from which another entity’s current or later name is derived.
-
D.
hasOriginalPart
Indicates that an entity includes a component or segment that is part of its initial, original composition.
-
E.
hasOriginalComposition
Indicates that one entity is the initial or primary compositional source or makeup of another entity.
- 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_69e0b506e59c8190849b71ed07929215 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e702d77b8081908ecfb05ab391fd39 |
completed | April 21, 2026, 4:53 a.m. |
| PD | Predicate disambiguation | batch_69e5dbfcd5e881908f1e4e0d2d237856 |
completed | April 20, 2026, 7:55 a.m. |
| PDg | Predicate description generation | batch_69e5e2e03d88819086f8b641656ad8b0 |
completed | April 20, 2026, 8:25 a.m. |
Created at: April 16, 2026, 2:49 p.m.