Triple
T6723863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Impact |
E153461
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Impact |
E153461
|
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: Impact | Statement: [Impact, nickname, Impact]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Impact Context triple: [Impact, nickname, Impact]
-
A.
Impact
chosen
Impact is the commonly used short name and nickname for the Montreal Impact, a professional soccer club based in Montreal, Canada.
-
B.
IMPACT
IMPACT is a scientific instrument suite on NASA's STEREO mission designed to study solar energetic particles and the Sun’s influence on the heliosphere.
-
C.
Impact Segment
Impact Segment was a recurring feature on the Fox News talk show "The O’Reilly Factor" that focused on in-depth analysis and commentary about major news stories and issues.
-
D.
Intensity
Intensity is a psychological suspense novel by Dean Koontz that follows a young woman’s harrowing struggle for survival against a relentless serial killer.
-
E.
Consequences
"Consequences" is a memoir by Canadian author and former First Lady Margaret Trudeau, in which she reflects on her personal struggles, relationships, and mental health.
- 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_69c6880afb988190ad88011b48ecfcba |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d13b296c8190bf54009063032c6d |
completed | March 27, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c700a34e248190ba9d73437b19a96d |
completed | March 27, 2026, 10:11 p.m. |
Created at: March 27, 2026, 2:08 p.m.