Triple
T9808469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dual Frequency Synthetic Aperture Radar |
E238209
|
entity |
| Predicate | measurementGoal |
P23062
|
FINISHED |
| Object | discriminate water ice from dry regolith |
—
|
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: discriminate water ice from dry regolith | Statement: [Dual Frequency Synthetic Aperture Radar, measurementGoal, discriminate water ice from dry regolith]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: measurementGoal Context triple: [Dual Frequency Synthetic Aperture Radar, measurementGoal, discriminate water ice from dry regolith]
-
A.
aimsToMeasure
chosen
Indicates that one entity is intended or designed to quantify, assess, or evaluate another entity or property.
-
B.
goalType
Indicates the specific category or nature of a goal associated with an entity or action.
-
C.
strategicGoal
Indicates that one entity represents a long-term objective or desired outcome that another entity is intentionally aiming to achieve or align actions toward.
-
D.
goalNumber
Indicates that an entity is associated with a specific target or objective quantified as a number.
-
E.
hasMaintenanceGoal
Indicates that an entity is associated with a specific objective or target related to its upkeep, repair, or ongoing maintenance activities.
- 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_69ca84defac48190abc1148804f184c1 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb21ef32c8190ab4b09d157798451 |
completed | April 2, 2026, 12:02 a.m. |
| PD | Predicate disambiguation | batch_69cd03dd2da881909052fbf29736a773 |
completed | April 1, 2026, 11:39 a.m. |
Created at: March 30, 2026, 8:29 p.m.