Triple
T12090254
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Into the Badlands |
E287922
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Aramis Knight |
E944739
|
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: Aramis Knight | Statement: [Into the Badlands, starring, Aramis Knight]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aramis Knight Context triple: [Into the Badlands, starring, Aramis Knight]
-
A.
Aramis Knight
chosen
Aramis Knight is an American actor best known for his role as Bean in the science fiction film adaptation of "Ender's Game."
-
B.
Aramis
Aramis is one of the three musketeers in Alexandre Dumas' classic novel, known for his elegance, wit, and religious vocation.
-
C.
Athos
Athos is a brooding, noble swordsman and one of the titular trio in Alexandre Dumas’ classic adventure novel "The Three Musketeers."
-
D.
Athos
Athos is a mountainous peninsula in northern Greece best known for its autonomous monastic community of Eastern Orthodox monasteries.
-
E.
Porthos
Porthos is Captain Jonathan Archer’s beloved beagle in Star Trek: Enterprise, often seen accompanying him aboard the starship Enterprise NX-01.
- 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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d915161f848190a6355c1e372eadaa |
completed | April 10, 2026, 3:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f66b2eb48190bae469d1dd82b119 |
completed | May 2, 2026, 1:04 p.m. |
Created at: April 8, 2026, 9:48 p.m.