Triple
T15424202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luis Felipe López |
E369462
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | López |
E197855
|
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: López | Statement: [Luis Felipe López, familyName, López]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: López Context triple: [Luis Felipe López, familyName, López]
-
A.
López
chosen
López is a common Spanish surname widely borne across Spain and Latin America.
-
B.
Lopez
Lopez is a municipality in the province of Quezon in the Philippines, known for its agricultural economy and coastal location.
-
C.
González
González is a common Spanish-language surname widely borne across Spain and Latin America, often associated with Iberian heritage.
-
D.
Sosa
Sosa is a Spanish-origin surname most famously associated with former Major League Baseball slugger Sammy Sosa.
-
E.
Pérez
Pérez is a common Spanish-language surname widely found in Spain and Latin America.
- 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_69d85a1849f48190bf898068b2806fae |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03ec032548190840b558dde6057c7 |
completed | April 16, 2026, 1:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff1a7ed0ec8190b8086f78df965b61 |
completed | May 9, 2026, 11:29 a.m. |
Created at: April 10, 2026, 3:20 a.m.