Triple
T19192940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Francisco Sá Carneiro |
E469888
|
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 Sá Carneiro, givenName, Francisco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Francisco Context triple: [Francisco Sá Carneiro, 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 one of the twin brothers and central tragic figures in Thornton Wilder’s novel "The Bridge of San Luis Rey," whose life and death help explore themes of love, fate, and divine purpose.
-
C.
Manuel
Manuel is a character from the work "The Builders," likely serving as one of its central figures within the story.
-
D.
Manuel
Manuel is a fictional character who appears in the film "The Psychiatrist."
-
E.
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.
- 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_69d8dd0ad9088190a173b32657ae2e7a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f8a3434881908acc4063a9ee9386 |
completed | April 20, 2026, 9:57 a.m. |
Created at: April 10, 2026, 12:07 p.m.