Triple
T7988698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luis Gonzalez |
E185747
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Luis |
E348349
|
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: Luis | Statement: [Luis Gonzalez, givenName, Luis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luis Context triple: [Luis Gonzalez, givenName, Luis]
-
A.
Luis
Luis is the Spanish given name of Louis I of Spain, an 18th-century Bourbon king who briefly ruled the country.
-
B.
Luis
chosen
Luis is a comedic supporting character in the Marvel Cinematic Universe, best known as Scott Lang’s fast-talking friend and former cellmate in the Ant-Man films.
-
C.
Jorge
Jorge is the birth name of Pope Francis, the head of the Roman Catholic Church and the first pope from the Americas.
-
D.
Jorge
Jorge is a masculine given name of Spanish and Portuguese origin, equivalent to George in English.
-
E.
Jorge
Jorge is a character portrayed by actor Giancarlo Esposito, known for his nuanced and often intense roles in film and television.
- 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_69ca829a2cfc819083d591d58ec04075 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3c4e47308190918b67fb6eac9046 |
completed | March 31, 2026, 3:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc63b96ed48190b752865ef3855e46 |
completed | April 1, 2026, 12:15 a.m. |
Created at: March 30, 2026, 5:16 p.m.