Triple
T17581664
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RoboCop 3 |
E428217
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Anne Lewis |
E910956
|
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: Anne Lewis | Statement: [RoboCop 3, character, Anne Lewis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anne Lewis Context triple: [RoboCop 3, character, Anne Lewis]
-
A.
Anne Lewis
chosen
Anne Lewis is a tough, principled Detroit police officer and RoboCop’s closest human partner and ally in the RoboCop franchise.
-
B.
Anna Massey
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.
-
C.
Rebecca Gibney
Rebecca Gibney is a New Zealand-born Australian actress known for her prominent roles in film and television, including acclaimed Australian dramas and comedies.
-
D.
Eleanor Bron
Eleanor Bron is a British actress and writer known for her distinctive, often imperious screen presence in film, television, and theatre.
-
E.
Edith Lesley
Edith Lesley was an American educator and founder of the teacher-training institution that evolved into Lesley University in Cambridge, Massachusetts.
- 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_69d889e1030481909950e140c63255b9 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e463ce8eb081909257be47d150aa04 |
completed | April 19, 2026, 5:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01ddef82d48190a5940f7da646c380 |
completed | May 11, 2026, 1:47 p.m. |
Created at: April 10, 2026, 5:50 a.m.