Triple
T11509376
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mariner 2 |
E272868
|
entity |
| Predicate | closestApproachTarget |
P13778
|
FINISHED |
| Object | Venus |
E19350
|
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: Venus | Statement: [Mariner 2, closestApproachTarget, Venus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Venus Context triple: [Mariner 2, closestApproachTarget, Venus]
-
A.
Venus
Venus is a Procter & Gamble personal care brand best known for its women's razors and shaving products.
-
B.
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.
-
C.
Venus
Venus is the Roman goddess of love, beauty, and fertility, often depicted as the divine ancestor and protector of Aeneas and the Roman people.
-
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 Romanian Black Sea seaside resort town in Constanța County, known for its beaches and tourism.
- 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_69d6aae2c3748190bed2ea50dfb160dc |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d86db65eb081908613a1002c6a4fb4 |
completed | April 10, 2026, 3:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e6852a23688190a3fe8e5508435661 |
completed | April 20, 2026, 7:57 p.m. |
Created at: April 8, 2026, 9:36 p.m.