Triple
T3502293
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sue Hendrickson |
E73996
|
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 Hendrickson, givenName, Sue]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sue Context triple: [Sue Hendrickson, 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.
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.
-
C.
Suzanne
"Suzanne" is a renowned song by Leonard Cohen, celebrated for its poetic lyrics and haunting melody.
-
D.
Suze
The Suze is a river in western Switzerland that flows through the Jura region and the city of Biel/Bienne before emptying into Lake Biel.
-
E.
Sally
Sally is the Allied reporting name for the Mitsubishi Ki-21, a Japanese twin-engine army bomber used extensively during World War II.
- 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbbef47988190b5b3fe2e452b9ac8 |
completed | March 8, 2026, 6:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b373d88dc08190a8508f990b01cf03 |
completed | March 13, 2026, 2:18 a.m. |
Created at: March 8, 2026, 3:18 p.m.