Triple
T12686415
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple Martin |
E303080
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Apple Martin |
E303080
|
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: Apple Martin | Statement: [Apple Martin, name, Apple Martin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Apple Martin Context triple: [Apple Martin, name, Apple Martin]
-
A.
Apple Martin
chosen
Apple Martin is the daughter of actress Gwyneth Paltrow and Coldplay frontman Chris Martin, known for her occasional appearances in the media and on her parents’ social platforms.
-
B.
Kit Packer
Kit Packer is best known as the wife of influential evangelical theologian J. I. Packer.
-
C.
Pincher Martin
Pincher Martin is a psychological novel by William Golding that follows a shipwrecked naval officer’s harrowing struggle for survival and sanity on a desolate rock in the Atlantic.
-
D.
Martz
Martz is a surname most notably associated with Mike Martz, an American football coach known for his innovative offensive strategies in the NFL.
-
E.
Mike Teavee
Mike Teavee is a television-obsessed, video game–addicted boy whose bratty behavior and fixation on screens lead to his comically disastrous fate during Willy Wonka’s factory tour.
- 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_69d7bdee64a08190801c6d470aefd723 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961d7cd4c81909521839ef5859799 |
completed | April 10, 2026, 8:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f671a8f068819086e2191439607f76 |
completed | May 2, 2026, 9:50 p.m. |
Created at: April 9, 2026, 5:21 p.m.