Triple
T290668
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Number One Observatory Circle |
E5983
|
entity |
| Predicate | hasGroundsFeature |
P7001
|
FINISHED |
| Object | lawns |
—
|
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: lawns | Statement: [Number One Observatory Circle, hasGroundsFeature, lawns]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGroundsFeature Context triple: [Number One Observatory Circle, hasGroundsFeature, lawns]
-
A.
hasGrounds
chosen
Indicates that one entity possesses or includes a physical area of land or outdoor space associated with it.
-
B.
hasGroundState
Indicates that an entity possesses a lowest-energy, most stable state in its energy configuration.
-
C.
hasUrbanFeature
Indicates that a place or area possesses a specific urban element or infrastructure feature (such as roads, parks, or buildings) as part of its built environment.
-
D.
hasNaturalFeature
Indicates that one entity possesses, contains, or is characterized by a particular natural feature (such as a mountain, river, forest, or coastline).
-
E.
hasShoreFeature
Indicates that a shore or coastline possesses a specific physical or environmental feature.
- 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_69a25946a7ac8190a78871c210213272 |
completed | Feb. 28, 2026, 2:56 a.m. |
| NER | Named-entity recognition | batch_69a25e4f78bc81909a64fd6aaec3b504 |
completed | Feb. 28, 2026, 3:17 a.m. |
| PD | Predicate disambiguation | batch_69a25b7d089081909810442c3d0182f6 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 3:02 a.m.