Triple
T1749695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark "Oz" Geist (13 Hours film character) |
E38410
|
entity |
| Predicate | hasMilitaryBackground |
P22805
|
FINISHED |
| Object | yes |
—
|
LITERAL 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: yes | Statement: [Mark "Oz" Geist (13 Hours film character), hasMilitaryBackground, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMilitaryBackground Context triple: [Mark "Oz" Geist (13 Hours film character), hasMilitaryBackground, yes]
-
A.
hasMilitaryBranch
Indicates that an entity is associated with, served in, or is part of a specific branch of a military organization.
-
B.
hasMilitaryServiceStart
Indicates the date or point in time when an entity’s period of military service began.
-
C.
hasMilitaryAssociation
chosen
Indicates a relationship in which an entity is connected or affiliated with a military organization, activity, or function.
-
D.
militaryStatus
Indicates the relationship between an entity and a military organization in terms of service condition, such as active duty, reserve, veteran, or non-military status.
-
E.
militaryBranchEligibility
Indicates that an entity meets the required conditions to serve in a specified branch of the military.
- F. None of above.
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_69a8862bdb2081908aefe831c8aa8017 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab630e7d008190a8c673665d9672bb |
completed | March 6, 2026, 11:28 p.m. |
| PD | Predicate disambiguation | batch_69aa61c5a18481909bc49e0c54d64314 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:31 p.m.