Triple
T35351823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seleucus (crater) |
E1020899
|
entity |
| Predicate | hasLROImagery |
P122998
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Seleucus (crater), hasLROImagery, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLROImagery Context triple: [Seleucus (crater), hasLROImagery, yes]
-
A.
hasImageryFrom
chosen
Indicates that one entity contains, incorporates, or is derived from the imagery produced or provided by another entity.
-
B.
hasColorImagery
Indicates that something includes or is characterized by visual elements emphasizing specific colors or color-based symbolism.
-
C.
hasRemoteSensingDataFrom
Indicates that one entity possesses or is associated with remote sensing data that was obtained from another entity or source.
-
D.
hasRemoteSensingData
Indicates that an entity possesses or is associated with data collected via remote sensing technologies (e.g., satellite or aerial sensors).
-
E.
hasImageryType
Indicates that one entity is associated with a specific kind or category of imagery (such as visual style, medium, or representation type) used to depict or describe it.
- F. None of above.
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_69f76decd95c8190ae428f6a19d535de |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f79533b88c8190934ec4cb21770e24 |
completed | May 3, 2026, 6:34 p.m. |
| PD | Predicate disambiguation | batch_69f79104f5b48190a496cdffde8472da |
completed | May 3, 2026, 6:16 p.m. |
Created at: May 3, 2026, 4:03 p.m.