Triple
T19397190
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Francisco Garcia |
E485221
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Francisco |
—
|
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: Francisco | Statement: [Francisco Garcia, givenName, Francisco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Francisco Context triple: [Francisco Garcia, givenName, Francisco]
-
A.
Francisco
chosen
Francisco is a masculine given name of Spanish and Portuguese origin, equivalent to Francis in English.
-
B.
Manuel
Manuel is the hapless, linguistically challenged Spanish waiter from the British sitcom "Fawlty Towers," known for his comedic misunderstandings and clashes with Basil Fawlty.
-
C.
Manuel
Manuel is a Spanish noble family name historically associated with medieval Castilian aristocracy.
-
D.
Manuel
Manuel is the central protagonist of the novel "Libro de Manuel," around whom the story’s political and personal themes revolve.
-
E.
Manuel
Manuel is a masculine given name of Hebrew origin, commonly used in Spanish- and Portuguese-speaking countries and derived from "Emmanuel," meaning "God is with us."
- 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_69d8e8d5162481909db12435d9535c1a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e62573f5788190a635b92121db2cf7 |
completed | April 20, 2026, 1:09 p.m. |
Created at: April 10, 2026, 1:36 p.m.