Triple
T622752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Little, Brown and Company |
E14548
|
entity |
| Predicate | notableAuthorPublished |
P7039
|
FINISHED |
| Object | Stephenie Meyer |
E69015
|
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: Stephenie Meyer | Statement: [Little, Brown and Company, notableAuthorPublished, Stephenie Meyer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stephenie Meyer Context triple: [Little, Brown and Company, notableAuthorPublished, Stephenie Meyer]
-
A.
Stephenie Meyer
chosen
Stephenie Meyer is an American author best known for writing the hugely popular Twilight vampire romance series, which was adapted into a successful film franchise.
-
B.
E. L. James
E. L. James is a British author best known for writing the bestselling erotic romance trilogy "Fifty Shades of Grey."
-
C.
Valeria Wasserman
Valeria Wasserman is a Brazilian linguist and translator best known as the wife of renowned intellectual Noam Chomsky.
-
D.
Jessica Barth
Jessica Barth is an American actress best known for playing Tami-Lynn in the comedy films "Ted" and "Ted 2."
-
E.
Alan Webber
Alan Webber is an American politician and former business magazine co-founder who serves as the mayor of Santa Fe, New Mexico.
- 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_69a4934b17c881909ace8270e8ddd202 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49e402d9c8190936896e3ebb6edc5 |
completed | March 1, 2026, 8:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a563cab73c819082b51d64d249143b |
completed | March 2, 2026, 10:17 a.m. |
Created at: March 1, 2026, 7:35 p.m.