Triple
T8832422
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GBAR |
E210176
|
entity |
| Predicate | relatedExperiment |
P37
|
FINISHED |
| Object | ATRAP |
E761920
|
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: ATRAP | Statement: [GBAR, relatedExperiment, ATRAP]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ATRAP Context triple: [GBAR, relatedExperiment, ATRAP]
-
A.
ATRAP
chosen
ATRAP is a CERN-based physics experiment focused on producing, trapping, and precisely studying antihydrogen atoms to test fundamental symmetries between matter and antimatter.
-
B.
ALTR
ALTR is the stock ticker symbol for Altera Corporation, a former leading manufacturer of programmable logic devices that was acquired by Intel.
-
C.
Atripé
Atripé was an ancient Egyptian town in Upper Egypt notable as the home of the influential Coptic monastic leader Shenoute.
-
D.
Atreseries
Atreseries is a Spanish television channel owned by Atresmedia that specializes in broadcasting TV series and fiction content.
-
E.
Ate
Ate is a populous district in the eastern part of Lima, Peru, known for its mix of industrial zones, residential areas, and growing commercial activity.
- 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_69ca8388549c819095fd94eadefbb007 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc605005788190a4df1fe317f3056a |
completed | April 1, 2026, 12:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfa069d7488190ade4caa15fa83cd9 |
completed | April 3, 2026, 11:11 a.m. |
Created at: March 30, 2026, 6:47 p.m.