Triple
T1917297
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elizabeth Siddal |
E40045
|
entity |
| Predicate | workedBeforeModeling |
P4325
|
FINISHED |
| Object | milliner's assistant |
—
|
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: milliner's assistant | Statement: [Elizabeth Siddal, workedBeforeModeling, milliner's assistant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: workedBeforeModeling Context triple: [Elizabeth Siddal, workedBeforeModeling, milliner's assistant]
-
A.
hasModelledFor
Indicates that one entity has served as a model for another entity, typically in a professional or representational context such as art, photography, or fashion.
-
B.
workedUnder
Indicates that one entity was hierarchically subordinate to and performed work under the supervision or authority of another entity.
-
C.
workedAs
chosen
Indicates that an entity held a particular job, role, or position, performing work in that capacity.
-
D.
hasWorkedIn
Indicates that a person has been employed or has performed work within a particular organization, location, or domain for some period of time.
-
E.
hasWorkedFor
Indicates that an entity has been employed by or has provided work or services to another entity.
- 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_69a8864298748190a2f2fd34f7ef8d77 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb2107fe48190bafff825f1f805ad |
completed | March 7, 2026, 5:05 a.m. |
| PD | Predicate disambiguation | batch_69abafed2ab481908920334e77b1021b |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:35 p.m.