Triple
T2018301
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eleazar ben Ya'ir |
E44045
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object | Jair |
E105696
|
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: Jair | Statement: [Eleazar ben Ya'ir, relative, Jair]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jair Context triple: [Eleazar ben Ya'ir, relative, Jair]
-
A.
Jair
chosen
Jair is a minor biblical judge of Israel mentioned in the Book of Judges, known for his leadership and his thirty sons who rode thirty donkeys and controlled thirty towns.
-
B.
Miguel Jontel Pimentel
Miguel Jontel Pimentel is an American singer, songwriter, and record producer best known for his genre-blending R&B music and hits like "Adorn."
-
C.
Mariano
Mariano is a masculine given name of Spanish and Portuguese origin, commonly used in various Spanish-speaking and Latin cultures.
-
D.
Jorge Guillermo
Jorge Guillermo is a Cuban-born American educator and former husband of Princess Christina of the Netherlands.
-
E.
Azevêdo
Azevêdo is a Portuguese-language surname commonly found in Brazil and Portugal, associated with several notable public figures.
- 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_69a8891201bc8190aca837be6de41579 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb8ce71788190ac21beff10b08122 |
completed | March 7, 2026, 5:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae0af1547481909d5f2ca9c4715ace |
completed | March 8, 2026, 11:49 p.m. |
Created at: March 4, 2026, 7:38 p.m.