Triple
T4904823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elimelech |
E109888
|
entity |
| Predicate | hasRelative |
P367
|
FINISHED |
| Object | Naomi |
E48334
|
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: Naomi | Statement: [Elimelech, hasRelative, Naomi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Naomi Context triple: [Elimelech, hasRelative, Naomi]
-
A.
Naomi
chosen
Naomi is a feminine given name of Hebrew origin meaning "pleasantness" or "delight."
-
B.
Naoma
Naoma is an unincorporated community and coal-mining town located in Raleigh County, West Virginia, United States.
-
C.
Nora
Nora is a feminine given name of Latin origin, often used independently or as a diminutive of names like Honora, Eleanor, or Leonora.
-
D.
Naomi King
Naomi King is the daughter of renowned American horror novelist Stephen King.
-
E.
Naima
Naima is a character in the gospel musical and film "Black Nativity," which reimagines the Nativity story through an African-American cultural and spiritual lens.
- 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_69bd441180708190ba42ffb44fea533a |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6e71a5c481909f5862bf497c3c9d |
completed | March 20, 2026, 3:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be89e3215081908120a8d307debbb3 |
completed | March 21, 2026, 12:06 p.m. |
Created at: March 20, 2026, 1:29 p.m.