Triple
T1806056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mercury Grand Marquis |
E40221
|
entity |
| Predicate | layoutDetail |
P32581
|
FINISHED |
| Object | front bench seat available |
—
|
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: front bench seat available | Statement: [Mercury Grand Marquis, layoutDetail, front bench seat available]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: layoutDetail Context triple: [Mercury Grand Marquis, layoutDetail, front bench seat available]
-
A.
scopeDetail
Indicates a more specific or refined characterization of the extent, boundaries, or coverage of something within a broader scope.
-
B.
stationLayout
Indicates the spatial arrangement and structural organization of elements within a station.
-
C.
areaComponent
Indicates that one area is a constituent or sub-area that forms part of a larger area.
-
D.
layoutEngine
Indicates the rendering or layout system responsible for arranging and positioning elements within a visual or document structure.
-
E.
depictionDetail
Indicates that one depiction provides additional detail, refinement, or a closer view of what is shown in another depiction.
- 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_69a88643a3388190a612f2ebe1fb29e7 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aba67721788190951beae25e885457 |
completed | March 7, 2026, 4:15 a.m. |
| PD | Predicate disambiguation | batch_69aa61d514c081908197ac1f7c7d7a88 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69aba67554788190b429f2b9f0a70310 |
completed | March 7, 2026, 4:15 a.m. |
Created at: March 4, 2026, 7:32 p.m.