Triple
T36440298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Marine 5: Battleground |
E897707
|
entity |
| Predicate | featuresFormerProfessionOfProtagonist |
P35945
|
FINISHED |
| Object | United States Marine |
E586111
|
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: United States Marine | Statement: [The Marine 5: Battleground, featuresFormerProfessionOfProtagonist, United States Marine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresFormerProfessionOfProtagonist Context triple: [The Marine 5: Battleground, featuresFormerProfessionOfProtagonist, United States Marine]
-
A.
featuresProtagonistOccupation
Indicates that the work’s main character has a specified occupation or job role.
-
B.
otherProtagonistOccupation
Indicates that another main character in the narrative has a specific occupation or job role.
-
C.
characterFormerOccupation
chosen
Indicates that a character previously held a specific occupation but no longer does.
-
D.
protagonistParentOccupation
Indicates the occupation or job held by the protagonist’s parent in the described context.
-
E.
portrayedProfessionOfCharacter
Indicates that one entity is the profession or occupation depicted as being held by a particular character.
- F. None of above.
Provenance (4 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_69f76e56636481908eda808ab0273401 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a39cfc6f34c8190ab4bf08fcb988e43 |
completed | June 23, 2026, 12:13 a.m. |
| PD | Predicate disambiguation | batch_6a037a0a54cc8190868c1bfa1590d1a6 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:10 p.m.