Triple
T17765182
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IR2 camera |
E443484
|
entity |
| Predicate | primaryTarget |
P815
|
FINISHED |
| Object | Venus |
—
|
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: Venus | Statement: [IR2 camera, primaryTarget, Venus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Venus Context triple: [IR2 camera, primaryTarget, Venus]
-
A.
Venus
chosen
Venus is the second planet from the Sun, known for its dense, toxic atmosphere, extreme surface temperatures, and bright visibility in Earth's sky.
-
B.
Venus
"Venus" is a pop studio album by Swedish singer Zara Larsson, showcasing her polished, dance-oriented sound and contemporary songwriting.
-
C.
Venus
Venus is a character in the action film "Crank: High Voltage," known for her involvement in the movie’s chaotic, high-energy storyline.
-
D.
Venus
"Venus" is a 2006 British comedy-drama film directed by Roger Michell, starring Peter O'Toole as an aging actor whose life is shaken up by his unexpected relationship with a young woman.
-
E.
Venus
Venus is a small suburban town within the greater Dallas–Fort Worth metropolitan area in Texas.
- 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_69d8b9edf16c8190a59ebd245d378f4f |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e485fb2a3c81908887d1d36aee942d |
completed | April 19, 2026, 7:36 a.m. |
Created at: April 10, 2026, 10:11 a.m.