Triple
T15904264
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frenzy |
E385668
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Anna Massey |
E440436
|
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: Anna Massey | Statement: [Frenzy, starring, Anna Massey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anna Massey Context triple: [Frenzy, starring, Anna Massey]
-
A.
Anna Massey
chosen
Anna Massey was an acclaimed English actress known for her nuanced performances in film, television, and theatre, including notable roles in psychological dramas and literary adaptations.
-
B.
Anne Lewis
Anne Lewis is a tough, principled Detroit police officer and RoboCop’s closest human partner and ally in the RoboCop franchise.
-
C.
Edith Lesley
Edith Lesley was an American educator and founder of the teacher-training institution that evolved into Lesley University in Cambridge, Massachusetts.
-
D.
Peggy Ashcroft
Peggy Ashcroft was a distinguished English stage and film actress renowned for her Shakespearean performances and her long, influential career in British theatre.
-
E.
Celia Johnson
Celia Johnson was a distinguished English actress best known for her nuanced, understated performances in classic British films such as "Brief Encounter."
- 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_69d86da686e4819097cbf3b1fc2d881d |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1563f74d88190a3d92ca0ad46e867 |
completed | April 16, 2026, 9:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0035450810819092796c556dfa8ed3 |
completed | May 10, 2026, 7:35 a.m. |
Created at: April 10, 2026, 4:52 a.m.