Triple
T7606872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terry Jeffords |
E180127
|
entity |
| Predicate | closeColleague |
P11349
|
FINISHED |
| Object | Rosa Diaz |
E136122
|
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: Rosa Diaz | Statement: [Terry Jeffords, closeColleague, Rosa Diaz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rosa Diaz Context triple: [Terry Jeffords, closeColleague, Rosa Diaz]
-
A.
Rosa Diaz
chosen
Rosa Diaz is a tough, enigmatic, and fiercely loyal NYPD detective known for her deadpan humor and intimidating presence on the sitcom "Brooklyn Nine-Nine."
-
B.
Carmen Vasquez
Carmen Vasquez is a supporting character in the 2000 crime-action film "Shaft," involved in the investigation led by detective John Shaft.
-
C.
Ria Torres
Ria Torres is a naturally gifted deception expert and protégé of Dr. Cal Lightman in the television series "Lie to Me," known for her intuitive ability to read microexpressions and detect lies.
-
D.
Angel Lopez
Angel Lopez is a music composer known for his work on the song "Every Hour."
-
E.
Angel Lopez
Angel Lopez is a music producer known for his work on the project "Hands On."
- 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_69c69f3567008190ab01d2ca7b53584a |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9fe10408190b1c12bb8f911cea8 |
completed | March 27, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c89a9dd42c8190bd03e960ebad8df9 |
completed | March 29, 2026, 3:21 a.m. |
Created at: March 27, 2026, 3:54 p.m.