Triple
T13694901
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Detective Jocelyn Carter |
E328358
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Jocelyn |
E284174
|
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: Jocelyn | Statement: [Detective Jocelyn Carter, givenName, Jocelyn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jocelyn Context triple: [Detective Jocelyn Carter, givenName, Jocelyn]
-
A.
Jocelyn
chosen
Jocelyn is a given name commonly used for people of any gender, often associated with the nickname "Jo."
-
B.
Jacklyn
Jacklyn is a feminine given name, typically considered a modern or variant form of Jacqueline.
-
C.
Yvette
Yvette is the given first name of the acclaimed American singer Chaka Khan.
-
D.
Jessamyn
Jessamyn is a feminine given name, often considered a variant of Jasmine or Jessamine.
-
E.
Elicia
Elicia is a secondary character in the Spanish tragicomedy "La Celestina," depicted as a young prostitute and companion of Celestina who contributes to the work’s themes of desire, greed, and social corruption.
- 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_69d8076ff62081908a7bd79889edd7a0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc8773f388190b2413b1e05fd5fd7 |
completed | April 12, 2026, 4:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d4f35888190b2c3df62bde1ce6e |
completed | May 3, 2026, 7:09 p.m. |
Created at: April 9, 2026, 9:54 p.m.