Triple
T20020111
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ramón Freire |
E494833
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Ramón |
—
|
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: Ramón | Statement: [Ramón Freire, givenName, Ramón]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ramón Context triple: [Ramón Freire, givenName, Ramón]
-
A.
Ramón
chosen
Ramón is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
-
B.
Ramon
Ramon is the surname of Ilan Ramon, the first Israeli astronaut and a payload specialist on the Space Shuttle Columbia.
-
C.
Ramon
Ramon is a municipality in the Philippine province of Isabela, known primarily as an agricultural area supported by irrigation from the nearby Magat Dam.
-
D.
Ramon
Ramon is a fictional evolved form or transformation of the character Raginmund, likely appearing in a fantasy or role-playing game setting.
-
E.
Raúl
Raúl is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
- 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_69da626bfd288190aa5d65098b6433ae |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6623f1598819097ad4fa392540901 |
completed | April 20, 2026, 5:28 p.m. |
Created at: April 11, 2026, 3:34 p.m.