Triple
T14363085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hall Pass |
E356152
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Sam Seig |
E594176
|
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 Seig | Statement: [Hall Pass, editedBy, Sam Seig]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sam Seig Context triple: [Hall Pass, editedBy, Sam Seig]
-
A.
Sam Seig
chosen
Sam Seig is a film editor known for his work on the crime drama movie "Never Die Alone."
-
B.
Jerry Seeman
Jerry Seeman was a prominent NFL official who served as a referee in multiple Super Bowls and later became the league’s Director of Officiating.
-
C.
Mark Seelig
Mark Seelig is a musician and composer known for his work in ambient and shamanic music, often featuring overtone and devotional chanting.
-
D.
Sean Sagar
Sean Sagar is a British actor known for roles in television dramas and action series, including a part in the NCIS franchise spin-off NCIS: Sydney.
-
E.
Kay Sievers
Kay Sievers is a German software engineer best known for his leading role in developing the systemd init system and related Linux userspace components.
- 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_69d8279163a081908aec45c0e3f1e02f |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8fabec088190bd8128371b29e958 |
completed | April 14, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd4c4cb0c4819094d59b4b1d43588b |
completed | May 8, 2026, 2:37 a.m. |
Created at: April 10, 2026, 1:15 a.m.