Triple
T1790533
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Martian |
E39484
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Mark Watney |
E197785
|
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: Mark Watney | Statement: [The Martian, character, Mark Watney]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Watney Context triple: [The Martian, character, Mark Watney]
-
A.
Mark Watney
chosen
Mark Watney is the resourceful astronaut and botanist who becomes stranded alone on Mars and must use his ingenuity to survive in Andy Weir’s science fiction novel "The Martian."
-
B.
Matt Kowalski
Matt Kowalski is a veteran NASA astronaut and spacewalker featured as a central character in the science fiction film "Gravity."
-
C.
Dr. Ryan Stone
Dr. Ryan Stone is the rookie medical engineer and astronaut portrayed by Sandra Bullock in the space survival film "Gravity."
-
D.
Rick Martinez
Rick Martinez is a fictional NASA astronaut and pilot in Andy Weir’s science fiction novel "The Martian."
-
E.
Fred Haise
Fred Haise is an American astronaut and test pilot best known as the lunar module pilot on the ill-fated Apollo 13 mission.
- 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_69a88631854081909723959921e45c2b |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa6512804c8190a5743c10bd37f83f |
completed | March 6, 2026, 5:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adbf54330c81908046b519a0297760 |
completed | March 8, 2026, 6:26 p.m. |
Created at: March 4, 2026, 7:32 p.m.