Triple
T15508289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Georgia (1995 film) |
E379138
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Max Perlich |
E173979
|
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: Max Perlich | Statement: [Georgia (1995 film), starring, Max Perlich]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Max Perlich Context triple: [Georgia (1995 film), starring, Max Perlich]
-
A.
Max Perlich
chosen
Max Perlich is an American character actor known for his offbeat, often quirky supporting roles in independent films and television series since the late 1980s.
-
B.
Michael Hecht
Michael Hecht is the birth name of Michael Howard, a British Conservative politician who served as Leader of the Opposition and Home Secretary.
-
C.
Michael Hecht
Michael Hecht is a scientist best known for leading NASA’s MOXIE experiment on the Perseverance rover, which demonstrates in-situ oxygen production on Mars.
-
D.
Jack Groetzinger
Jack Groetzinger is an American entrepreneur best known as a co-founder of the mobile-focused ticket marketplace SeatGeek.
-
E.
Michael Neeleman
Michael Neeleman is a notable individual recognized as a bearer of the Neeleman surname, likely distinguished in a professional or public context.
- 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_69d85cd53a7c819080f5b9042c4c199e |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03fcea8888190a7b69aca360183c3 |
completed | April 16, 2026, 1:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff366e472c819093472da2a49593c6 |
completed | May 9, 2026, 1:28 p.m. |
Created at: April 10, 2026, 3:55 a.m.