Triple
T19521413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Perfect Score |
E488408
|
entity |
| Predicate | mainSubject |
P3
|
FINISHED |
| Object | SAT |
—
|
NE NERFINISHED |
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: SAT | Statement: [The Perfect Score, mainSubject, SAT]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SAT Context triple: [The Perfect Score, mainSubject, SAT]
-
A.
SAT
SAT is Mexico’s federal tax administration authority responsible for collecting taxes, overseeing customs, and enforcing fiscal regulations.
-
B.
SAT
chosen
The SAT is a standardized college admissions test widely used in the United States to assess high school students' readiness for undergraduate study.
-
C.
SAT
SAT is the three-letter IATA airport code for San Antonio International Airport, a major commercial airport serving San Antonio, Texas.
-
D.
SAT
SAT is the abbreviation for Japan’s elite Special Assault Team, a specialized police tactical unit trained for counterterrorism and high-risk operations.
-
E.
SAT
SAT (the Boolean satisfiability problem) is the fundamental decision problem of determining whether there exists an assignment of truth values that makes a given Boolean formula evaluate to true.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8da8bec819081f400199491ccc3 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e635a0b37c8190b70b7427c2e85f59 |
completed | April 20, 2026, 2:18 p.m. |
Created at: April 10, 2026, 1:40 p.m.