Triple
T14026998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lorena Bernal |
E337485
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Bernal |
E120640
|
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: Bernal | Statement: [Lorena Bernal, familyName, Bernal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bernal Context triple: [Lorena Bernal, familyName, Bernal]
-
A.
Bernal
chosen
Bernal is a Spanish given name most famously borne by the conquistador and chronicler Bernal Díaz del Castillo, known for his detailed account of the conquest of Mexico.
-
B.
Pateros
Pateros is the smallest and only landlocked municipality in Metro Manila, Philippines, known for its duck-raising industry and production of balut.
-
C.
Aravena
Aravena is a Chilean surname most prominently associated with Alejandro Aravena, the renowned architect and Pritzker Prize laureate.
-
D.
Belen
Belen is a small city in central New Mexico known as a regional transportation hub and bedroom community for the Albuquerque metropolitan area.
-
E.
Potrero
Potrero is a metro station in Mexico City that serves passengers on Line 3 of the Mexico City Metro system.
- 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_69d81c6543a48190bd5ba93d7419e797 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2fa830ac81908cb7df7c9e81e42a |
completed | April 14, 2026, 12:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fbc333b7a08190b4f121fef69f7513 |
completed | May 6, 2026, 10:39 p.m. |
Created at: April 9, 2026, 10:20 p.m.