Triple
T15622423
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miss Jenny Du Pre |
E375589
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Jenny |
E996088
|
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: Jenny | Statement: [Miss Jenny Du Pre, hasGivenName, Jenny]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jenny Context triple: [Miss Jenny Du Pre, hasGivenName, Jenny]
-
A.
Jenny
Jenny is the main character of the story "Mosquitoes," around whom the narrative and its central events revolve.
-
B.
Jenny
"Jenny" is a narrative poem by Dante Gabriel Rossetti that explores themes of desire, morality, and Victorian attitudes toward prostitution through a reflective monologue addressed to a fallen woman.
-
C.
Jenny
chosen
Jenny is a common feminine given name used in English-speaking countries, often as a diminutive of Jennifer.
-
D.
Jenny
Jenny is a central fictional character in the Australian television drama series "The Newsreader," which follows the turbulent personal and professional lives of broadcast journalists in the 1980s.
-
E.
Jenny
"Jenny" is a French film featuring actress Sylvia Bataille in a significant role.
- 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_69d85ccf2794819096cda4cbcb02d478 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e9a95f08190b0013ba1428849d3 |
completed | April 16, 2026, 2:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff5f3da754819085a6bd9876b12c65 |
completed | May 9, 2026, 4:22 p.m. |
Created at: April 10, 2026, 4:14 a.m.