Triple
T14310256
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Susan Pringle Frost |
E354808
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Susan |
E559215
|
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: Susan | Statement: [Susan Pringle Frost, givenName, Susan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Susan Context triple: [Susan Pringle Frost, givenName, Susan]
-
A.
Susan
Susan is a friendly human character on Sesame Street who often interacts warmly with Big Bird and the other residents of the neighborhood.
-
B.
Susan
Susan is the birth name of American actress Sigourney Weaver, renowned for her iconic roles in science fiction and horror films such as the Alien franchise.
-
C.
Susan
Susan is a supporting character in the "Nosedive" episode of the anthology television series Black Mirror.
-
D.
Susan
chosen
Susan is a feminine given name of Hebrew origin meaning "lily" that has been widely used in English-speaking countries.
-
E.
Susan
Susan is the given name of American painter and photographer Susan Macdowell Eakins, known for her portraits and still lifes.
- 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_69d8278ed42c8190b9f882dcce611347 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de85b26da48190a96e2f60ace51335 |
completed | April 14, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd3d2e2444819090252684673ff3df |
completed | May 8, 2026, 1:32 a.m. |
Created at: April 10, 2026, 1:12 a.m.