Triple
T7357058
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suits |
E169651
|
entity |
| Predicate | hasSpinOff |
P7226
|
FINISHED |
| Object | Pearson |
E32445
|
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: Pearson | Statement: [Suits, hasSpinOff, Pearson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pearson Context triple: [Suits, hasSpinOff, Pearson]
-
A.
Pearson
chosen
Pearson is a major British multinational publishing and education company known for its textbooks, assessments, and digital learning solutions worldwide.
-
B.
Prentice Hall
Prentice Hall is a major American educational and professional publishing company known for its textbooks and academic titles across a wide range of disciplines.
-
C.
McGraw-Hill
McGraw-Hill is a major American educational publishing company known for producing textbooks and academic resources across a wide range of disciplines.
-
D.
Cengage Learning
Cengage Learning is a major educational content and technology company that produces textbooks, digital learning solutions, and course materials for higher education and professional markets worldwide.
-
E.
Harcourt
Harcourt is an English surname historically associated with a prominent aristocratic family involved in British politics and public life.
- 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_69c68a59f2288190877ca15c19b1e822 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f13a62e48190a2d1781a630aa9f0 |
completed | March 27, 2026, 9:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7faa6a5d88190b969b7783edc67b7 |
completed | March 28, 2026, 3:58 p.m. |
Created at: March 27, 2026, 3:06 p.m.