Triple
T38624057
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake LBJ shoreline |
E936954
|
entity |
| Predicate | crossesCounties |
P97417
|
FINISHED |
| Object | Llano County, Texas |
—
|
NE NERFINISHED |
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: Llano County, Texas | Statement: [Lake LBJ shoreline, crossesCounties, Llano County, Texas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: crossesCounties Context triple: [Lake LBJ shoreline, crossesCounties, Llano County, Texas]
-
A.
crossesStateBorders
Indicates that the referenced entity extends into or passes through more than one state’s territorial boundaries.
-
B.
crossesRegion
Indicates that an entity moves through or passes across the spatial extent of a specified region.
-
C.
crossesBetween
Indicates that one entity passes from one side of a second entity to the other, traversing the space between two reference points or boundaries associated with that second entity.
-
D.
crossesJurisdictions
chosen
Indicates that an action, process, or entity extends beyond a single legal or administrative authority and involves multiple jurisdictions.
-
E.
crossCount
Indicates the number of times one entity crosses or intersects another within a given context.
- 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_69f76ed403208190b862dc795171353f |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fd474b7e788190a9bb9b542d878f60 |
completed | May 8, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69fd46d8b2f0819099d92d72c902f60e |
completed | May 8, 2026, 2:13 a.m. |
Created at: May 3, 2026, 4:32 p.m.