Triple
T2447003
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | California tidelands |
E53617
|
entity |
| Predicate | typicalUses |
P2529
|
FINISHED |
| Object | navigation |
—
|
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: navigation | Statement: [California tidelands, typicalUses, navigation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalUses Context triple: [California tidelands, typicalUses, navigation]
-
A.
usageType
chosen
Indicates the specific manner, purpose, or context in which something is used or intended to be used.
-
B.
widelyUsedIn
Indicates that something is commonly or extensively utilized within a particular context, domain, or group.
-
C.
typicalFeatures
Indicates that the related entities are characteristic or commonly occurring features or attributes of something.
-
D.
eligibleUses
Indicates the types of actions, purposes, or contexts in which something is permitted or qualified to be used.
-
E.
typicalTools
Indicates that the related tools are commonly or characteristically used to perform the associated activity, task, or function.
- 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_69ab495d227c8190b26ae6548eeb1019 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abcabf477081908512df63ac1a6414 |
completed | March 7, 2026, 6:50 a.m. |
| PD | Predicate disambiguation | batch_69abc5ad8e588190b97c4cd7cf575043 |
completed | March 7, 2026, 6:29 a.m. |
Created at: March 6, 2026, 9:43 p.m.