Triple
T1623539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Lady of Shalott (painting) |
E35085
|
entity |
| Predicate | depictsTextualSource |
P1581
|
FINISHED |
| Object | lines from Tennyson’s poem describing the Lady in the boat |
—
|
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: lines from Tennyson’s poem describing the Lady in the boat | Statement: [The Lady of Shalott (painting), depictsTextualSource, lines from Tennyson’s poem describing the Lady in the boat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: depictsTextualSource Context triple: [The Lady of Shalott (painting), depictsTextualSource, lines from Tennyson’s poem describing the Lady in the boat]
-
A.
depicts
chosen
Indicates that one entity visually represents, portrays, or shows another entity.
-
B.
depictionContext
Indicates the situational or environmental setting in which something is depicted or represented.
-
C.
depictsName
Indicates that something visually represents or portrays the name of an entity.
-
D.
literarySource
Indicates that one entity serves as the written or literary origin, reference, or basis for another entity.
-
E.
depictionType
Indicates the specific manner or style in which something is visually represented or depicted.
- 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_69a886023194819080a3fccd6e325d0e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aaf4a0ef748190ae52b9656474c0ef |
completed | March 6, 2026, 3:37 p.m. |
| PD | Predicate disambiguation | batch_69a907c731808190a1d998155041b3c1 |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.