Triple
T16257517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Venus Express |
E394668
|
entity |
| Predicate | instrument |
P792
|
FINISHED |
| Object | MAG |
E394670
|
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: MAG | Statement: [Venus Express, instrument, MAG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MAG Context triple: [Venus Express, instrument, MAG]
-
A.
MAG
MAG is a major British airport operator that owns and manages several UK airports, including Manchester Airport.
-
B.
MAG
MAG is the National Rail station code for Maghull railway station in Merseyside, England.
-
C.
MAG
MAG is the parent company of Malaysia Airlines and related aviation businesses, overseeing the group’s airline, cargo, and aviation services operations.
-
D.
MAG
chosen
MAG is the magnetometer instrument aboard the European Space Agency’s Venus Express spacecraft, designed to measure Venus’s magnetic field and its interaction with the solar wind.
-
E.
MAG
MAG is an international humanitarian organization that works to clear landmines and unexploded ordnance and make land safe for communities affected by conflict.
- 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_69e2459b1624819086bf681075097235 |
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.