Triple
T14533967
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tempestt Bledsoe |
E340988
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Bledsoe |
E560999
|
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: Bledsoe | Statement: [Tempestt Bledsoe, familyName, Bledsoe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bledsoe Context triple: [Tempestt Bledsoe, familyName, Bledsoe]
-
A.
Bledsoe
chosen
Bledsoe is a surname most notably associated with American professional basketball player Eric Bledsoe.
-
B.
Woody Bledsoe
Woody Bledsoe was an American mathematician and computer scientist recognized as a pioneer in artificial intelligence, particularly in automated theorem proving and pattern recognition.
-
C.
A. Bledsoe
A. Bledsoe was an early settler and community leader credited with establishing the town of Lancaster in Texas.
-
D.
Big Sam
Big Sam is a loyal and strong enslaved field hand from Tara plantation in Margaret Mitchell’s novel "Gone with the Wind."
-
E.
Memphis Raines
Memphis Raines is the master car thief protagonist of the 2000 action film "Gone in 60 Seconds," known for his high-speed heists and leadership of a skilled crew.
- 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_69d822dac79c8190a84a073f3cbaced5 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dea053f9bc8190901b9d321811d881 |
completed | April 14, 2026, 8:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd7a589d488190b4a192f33d11092d |
completed | May 8, 2026, 5:53 a.m. |
Created at: April 10, 2026, 1:22 a.m.