Triple
T14026997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lorena Bernal |
E337485
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Lorena |
E270035
|
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: Lorena | Statement: [Lorena Bernal, givenName, Lorena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lorena Context triple: [Lorena Bernal, givenName, Lorena]
-
A.
Lorena
chosen
Lorena is a city in the state of São Paulo, Brazil, known for hosting a campus of the University of São Paulo.
-
B.
Carmelita
Carmelita is the fiery, comedic Mexican heroine portrayed by Lupe Vélez in the 1940s "Mexican Spitfire" film series.
-
C.
Carmelita
"Carmelita" is a melancholic country-rock song written by Warren Zevon, best known for its narrative of a down-and-out heroin addict in Los Angeles.
-
D.
Lauretta
Lauretta is a young soprano role in Puccini’s one-act opera *Gianni Schicchi*, best known for singing the famous aria “O mio babbino caro.”
-
E.
Tonina
Tonina is an ancient Maya archaeological site in Chiapas, Mexico, known for its towering acropolis, intricate relief sculptures, and significant role in Classic-period Maya politics.
- 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.