Triple
T21027070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zone of Avoidance |
E517966
|
entity |
| Predicate | alsoAffects |
P142521
|
FINISHED |
| Object | near‑infrared observations |
—
|
LITERAL 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: near‑infrared observations | Statement: [Zone of Avoidance, alsoAffects, near‑infrared observations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: alsoAffects Context triple: [Zone of Avoidance, alsoAffects, near‑infrared observations]
-
A.
areAffectedBy
Indicates that one entity experiences an effect, influence, or impact as a result of another entity or event.
-
B.
affectsRelationshipBetween
Indicates that one entity causes a change or influence on the nature, quality, or status of the relationship between two or more other entities.
-
C.
affectsVariant
Indicates that one entity has an influence or impact on a specific variant or version of another entity.
-
D.
affectsBenefit
Indicates that one entity has an influence on, modifies, or determines the benefit or advantage received by another entity.
-
E.
affectsProgram
Indicates that one entity produces an influence or change on a program, altering its behavior, state, or outcome.
- F. None of above. chosen
Provenance (4 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_69e0b503275c8190afd9a163f997c709 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fc7d93908190a2c29a4051fb5acc |
completed | April 21, 2026, 4:26 a.m. |
| PD | Predicate disambiguation | batch_69e5dbf274ac81909bbf245627dc8fdc |
completed | April 20, 2026, 7:55 a.m. |
| PDg | Predicate description generation | batch_69e5e2df1a888190b5b478e76bdf7fdf |
completed | April 20, 2026, 8:25 a.m. |
Created at: April 16, 2026, 1:55 p.m.