Triple
T16258283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Curiosity |
E394685
|
entity |
| Predicate | instrument |
P792
|
FINISHED |
| Object | MARDI |
E86326
|
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: MARDI | Statement: [Curiosity, instrument, MARDI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MARDI Context triple: [Curiosity, instrument, MARDI]
-
A.
MARDI
chosen
MARDI is a descent imaging camera on NASA's Curiosity rover that captured high-resolution video of the rover's landing on Mars and helps document the geology of its landing site.
-
B.
Mardi
"Mardi" is an 1849 novel by Herman Melville, known as his first major foray into philosophical and allegorical fiction set in a fantastical South Seas archipelago.
-
C.
Mardi
Mardi is the nickname of Brenda Olivia "Mardi" Nowak, an individual known by this shorter, familiar name.
-
D.
Marceline Day
Marceline Day was an American silent film actress best known for her leading roles in 1920s comedies and dramas, including opposite Buster Keaton.
-
E.
Muanda
Muanda is a coastal town in the Democratic Republic of the Congo situated near the mouth of the Congo River on the Atlantic Ocean.
- 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_69d87f221d8081909b0b2063e7528ba2 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e245c1fa208190995feaeba766b45f |
completed | April 17, 2026, 2:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a000eebcfe481909822290d3a7b361c |
completed | May 10, 2026, 4:51 a.m. |
Created at: April 10, 2026, 5:04 a.m.