Triple
T13861611
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Newsreader |
E333208
|
entity |
| Predicate | hasMainCharacter |
P1183
|
FINISHED |
| Object | Sam |
E64126
|
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: Sam | Statement: [The Newsreader, hasMainCharacter, Sam]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sam Context triple: [The Newsreader, hasMainCharacter, Sam]
-
A.
Sam
chosen
Sam is a person whose given name is Sam.
-
B.
Sam
Sam is the iconic, burlap-sack-masked embodiment of Halloween who enforces the holiday’s rules in the Trick 'r Treat horror franchise.
-
C.
Simon
Simon is a common masculine given name of Hebrew origin, widely used in many cultures and languages.
-
D.
Simon
Simon is a fictional character from the animated television series "The Seasons."
-
E.
Simon
Simon is the young, initially timid but ultimately heroic protagonist of the anime series Tengen Toppa Gurren Lagann, known for piloting powerful mecha and embodying themes of growth and determination.
- 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_69d81c5ced9c8190b0e9bcc6effe5959 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de05c20db88190acb842748aa01039 |
completed | April 14, 2026, 9:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c70c41f081908b6af3f419d8468d |
completed | May 3, 2026, 10:07 p.m. |
Created at: April 9, 2026, 10:14 p.m.