Triple
T20664483
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | War (Avatar track) |
E507848
|
entity |
| Predicate | title |
P38
|
FINISHED |
| Object | War |
—
|
NE NERFINISHED |
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: War | Statement: [War (Avatar track), title, War]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: War Context triple: [War (Avatar track), title, War]
-
A.
War
War is a small town in McDowell County, West Virginia, known as one of the southernmost communities in the state and for its history as a coal mining town.
-
B.
War
chosen
"War" is a musical track from the film score of James Cameron's 2009 science fiction epic "Avatar," composed by James Horner.
-
C.
War
"War" is a nonfiction book by Sebastian Junger that chronicles the experiences of American soldiers in Afghanistan’s Korengal Valley, exploring the psychology, brotherhood, and brutality of modern combat.
-
D.
War
War is an American funk and soul band best known for their 1970s hits blending rock, jazz, Latin, and R&B influences, including songs like "Low Rider" and "Why Can't We Be Friends?".
-
E.
War
"War" is a film featuring Swiss actress Ella Rumpf in a prominent role.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4c059bc81908ea762cd73ea4424 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6b2f541bc8190ac7946b91647f2b0 |
completed | April 20, 2026, 11:12 p.m. |
Created at: April 16, 2026, 11:44 a.m.