Triple
T3316787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sienna Miller |
E69700
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Miller |
E5201
|
NE 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: Miller | Statement: [Sienna Miller, familyName, Miller]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miller Context triple: [Sienna Miller, familyName, Miller]
-
A.
Miller
chosen
Miller is a common English and Scottish occupational surname historically given to people who worked in grain mills.
-
B.
Millard
Millard is the given name of Millard Fillmore, the 13th president of the United States.
-
C.
Smith
Smith is a common English surname borne by numerous notable individuals across diverse fields such as politics, arts, sports, and academia.
-
D.
Millner
Millner is an English occupational surname historically associated with people who made or sold hats or millinery goods.
-
E.
Mill
Mill is a village in the Dutch province of North Brabant, known for its historical sites and rural character.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ad85a0bb048190a5458d2738012d61 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb11230b881908f5b554323729cc5 |
completed | March 8, 2026, 5:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2f3fa347481909432bd0178e7ea57 |
completed | March 12, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:11 p.m.