Triple
T19803158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emmanuel Pelaez |
E475737
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Emmanuel |
—
|
NE NERFINISHED |
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: Emmanuel | Statement: [Emmanuel Pelaez, givenName, Emmanuel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Emmanuel Context triple: [Emmanuel Pelaez, givenName, Emmanuel]
-
A.
Emmanuel
Emmanuel is a Belgian prince, a member of the royal family of Belgium and the son of King Philippe and Queen Mathilde.
-
B.
Emmanuel
chosen
Emmanuel is a masculine given name of Hebrew origin meaning "God is with us," used in various languages and cultures.
-
C.
Immanuel
Immanuel is the given name of the influential German philosopher Immanuel Kant, a central figure in modern Western philosophy.
-
D.
Emanuel
Emanuel is a surname most prominently associated with Rahm Emanuel, the American politician and former mayor of Chicago.
-
E.
Emmanuel Kadosh
Emmanuel Kadosh is a cinematographer known for his work on the adventure-comedy film "The Lost City."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51bc4208190a1c57d8c5d1b15e4 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e654266e18819085698aed8b0e2ba8 |
completed | April 20, 2026, 4:28 p.m. |
Created at: April 10, 2026, 1:49 p.m.