Triple
T4885642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mariner 4 |
E109432
|
entity |
| Predicate | numberOfImagesReturned |
P59620
|
FINISHED |
| Object | 22 |
—
|
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: 22 | Statement: [Mariner 4, numberOfImagesReturned, 22]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfImagesReturned Context triple: [Mariner 4, numberOfImagesReturned, 22]
-
A.
numberOfFiguresDepicted
Indicates the total count of distinct figures shown within a given depiction or representation.
-
B.
displayCount
Indicates the number of times something is shown or presented, typically within a given context or interface.
-
C.
estimatedNumberOfPaintings
Indicates the approximate count of paintings associated with an entity, rather than an exact, verified number.
-
D.
filmStripCount
Indicates the number of film strips associated with or contained in a given entity or context.
-
E.
numberOfMembersReturned
Indicates the quantity of members that are provided or yielded as a result of an operation or query.
- 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_69bd440f71348190b99938e59fb7f9a1 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6de3718881908521968fa6e6b444 |
completed | March 20, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69bd6c2be5e881909f6ec9c3bcde49f3 |
completed | March 20, 2026, 3:47 p.m. |
| PDg | Predicate description generation | batch_69bd6d5976a081909090c0c263f6e9b7 |
completed | March 20, 2026, 3:52 p.m. |
Created at: March 20, 2026, 1:27 p.m.