Triple
T20034773
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sir Stephen Cleobury |
E497224
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Emma Disley |
—
|
NE NERFINISHED |
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: Emma Disley | Statement: [Sir Stephen Cleobury, spouse, Emma Disley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Emma Disley Context triple: [Sir Stephen Cleobury, spouse, Emma Disley]
-
A.
Emma Disley
chosen
Emma Disley is known as the spouse of the late Sir Stephen Cleobury, the renowned British choral conductor and longtime Director of Music at King's College, Cambridge.
-
B.
May Davies
May Davies was the mother of Romanian-born American actor and producer John Houseman.
-
C.
Jenny Dennison
Jenny Dennison is a character in the Hocus Pocus franchise, known as the younger sister of Dani Dennison.
-
D.
Tess Carlisle
Tess Carlisle is the wealthy, strong-willed widow of a U.S. senator whose contentious relationship with her Secret Service detail drives the plot of the film "Guarding Tess."
-
E.
Emma Cleasby
Emma Cleasby is a British actress best known for her role in the cult werewolf horror film "Dog Soldiers."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da627278c88190babe4297a9df1236 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e662e76f8481909c006921cbbfd060 |
completed | April 20, 2026, 5:31 p.m. |
Created at: April 11, 2026, 3:36 p.m.