Triple
T6349682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sue Monk Kidd |
E142836
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Sue |
E322092
|
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: Sue | Statement: [Sue Monk Kidd, givenName, Sue]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sue Context triple: [Sue Monk Kidd, givenName, Sue]
-
A.
Sue
chosen
Sue is the given name of Sue Storm, the Invisible Woman and a central member of Marvel’s superhero team the Fantastic Four.
-
B.
Sue
Sue is the tough, resilient male protagonist of the humorous country song "A Boy Named Sue," whose life is shaped by the hardships caused by his traditionally feminine name.
-
C.
Suzanne
"Suzanne" is a renowned song by Leonard Cohen, celebrated for its poetic lyrics and haunting melody.
-
D.
Suzanne
Suzanne is a central character in Steve Martin’s play "Picasso at the Lapin Agile," representing a young woman entangled romantically with both Picasso and other men in the bohemian Parisian setting.
-
E.
Suze
Suze is the nickname of Suze Rotolo, an American artist and political activist best known for her relationship with Bob Dylan in the early 1960s.
- 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_69c008d6dcbc8190aa1c2f1fd8916b42 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067bcec2c8190bb383605847b0f0b |
completed | March 22, 2026, 10:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6044fcd288190abdc5746e2904928 |
completed | March 27, 2026, 4:15 a.m. |
Created at: March 22, 2026, 4:31 p.m.