Triple
T12277748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margery Sharp |
E292633
|
entity |
| Predicate | wrote |
P2831
|
FINISHED |
| Object | The Turret |
E976200
|
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: The Turret | Statement: [Margery Sharp, wrote, The Turret]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: The Turret Context triple: [Margery Sharp, wrote, The Turret]
-
A.
The Turret
chosen
The Turret is a novel by British author Margery Sharp, best known for its blend of sharp social observation and character-driven storytelling.
-
B.
The Fortress
The Fortress is a South Korean historical drama film depicting the Joseon court’s struggle for survival during the Qing invasion, directed by Hwang Dong-hyuk.
-
C.
The Fortress
The Fortress is a popular nickname for MAPFRE Stadium, the historic soccer-specific home of the Columbus Crew in Major League Soccer.
-
D.
Turretin
Turretin is a notable Reformed theologian surname most famously associated with Francis Turretin, a 17th-century Genevan scholastic theologian.
-
E.
Fortress
A fortress is a heavily fortified defensive structure, often with thick walls, towers, and battlements, built to protect people and strategic locations from attack.
- 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_69d6ab6856488190b5d31178d5015f8e |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91cf06cf08190ac8671dd9bbed03d |
completed | April 10, 2026, 3:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62a959e7c8190a005f20728cb71e0 |
completed | May 2, 2026, 4:47 p.m. |
Created at: April 8, 2026, 9:52 p.m.