Triple
T2373209
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wide Field and Planetary Camera 2 |
E46135
|
entity |
| Predicate | numberOfFilters |
P38758
|
FINISHED |
| Object | approximately 48 |
—
|
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: approximately 48 | Statement: [Wide Field and Planetary Camera 2, numberOfFilters, approximately 48]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFilters Context triple: [Wide Field and Planetary Camera 2, numberOfFilters, approximately 48]
-
A.
numberOfModules
Indicates the total count of modules associated with a given entity.
-
B.
numberOfIndicators
Indicates the total count of indicators associated with or relevant to a given entity or context.
-
C.
numberOfMarkers
Indicates the quantity or count of markers associated with a given entity or context.
-
D.
numberOfTracks
Indicates the quantity of tracks associated with a given entity.
-
E.
numberOfInstances
Indicates the quantity or count of distinct occurrences or instances associated with a given entity or context.
- 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_69a88a145268819083e2736cb835c696 |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abca4d89248190be7d712d5fa8382b |
completed | March 7, 2026, 6:48 a.m. |
| PD | Predicate disambiguation | batch_69abc59d82f08190b7c36982d1ae783d |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abca4c937c8190b4dfca716b868d4f |
completed | March 7, 2026, 6:48 a.m. |
Created at: March 4, 2026, 7:56 p.m.