Triple
T12529869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tony Romo |
E299532
|
entity |
| Predicate | hasRelative |
P367
|
FINISHED |
| Object | Jossalyn Romo |
E299532
|
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: Jossalyn Romo | Statement: [Tony Romo, hasRelative, Jossalyn Romo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jossalyn Romo Context triple: [Tony Romo, hasRelative, Jossalyn Romo]
-
A.
Jossalyn Romo
chosen
Jossalyn Romo is known as the sister of former Dallas Cowboys quarterback and NFL broadcaster Tony Romo.
-
B.
Angelica Fuentes
Angelica Fuentes is a Mexican businesswoman and philanthropist known for her leadership roles in the energy sector and in professional soccer, as well as her advocacy for women's empowerment in Latin America.
-
C.
Daphne Zuniga
Daphne Zuniga is an American actress best known for her roles in the sci-fi parody film "Spaceballs" and the television series "Melrose Place."
-
D.
Marisa Ramirez
Marisa Ramirez is an American actress best known for her role as Detective Maria Baez on the television series "Blue Bloods."
-
E.
Lauren Vélez
Lauren Vélez is an American actress best known for her role as Lieutenant Maria LaGuerta on the television series "Dexter."
- 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_69d6ada5cdd48190860d9ce30aff69be |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d95469d100819087c83bc55e3ec9ce |
completed | April 10, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6c0d6947c819080d33199d331724c |
completed | May 3, 2026, 3:28 a.m. |
Created at: April 8, 2026, 9:57 p.m.