Triple
T7666345
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Effective STL |
E173632
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Scott Meyers |
E173630
|
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: Scott Meyers | Statement: [Effective STL, author, Scott Meyers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scott Meyers Context triple: [Effective STL, author, Scott Meyers]
-
A.
Scott Meyers
chosen
Scott Meyers is a renowned software engineer and author best known for his influential books on effective C++ programming and software design.
-
B.
Herb Sutter
Herb Sutter is a prominent C++ expert, author, and standards committee member known for his influential writings and contributions to modern C++ design and concurrency.
-
C.
Andrew Koenig
Andrew Koenig was an American actor and activist best known for his role as Richard "Boner" Stabone on the television series "Growing Pains."
-
D.
Bjarne Stroustrup
Bjarne Stroustrup is a Danish computer scientist best known as the creator of the C++ programming language.
-
E.
Skip Hinnant
Skip Hinnant is an American actor and voice actor best known for his work on the children's television show "The Electric Company" and various animated productions.
- 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_69c699562484819086752091e3164a27 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c701c1383c8190ab5bf803bd6211a9 |
completed | March 27, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8a2206664819085c6825e63eadd6f |
completed | March 29, 2026, 3:53 a.m. |
Created at: March 27, 2026, 4 p.m.