Triple
T27633049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hamburg, Arkansas |
E696391
|
entity |
| Predicate | hasAreaWaterBodyNearby |
P49291
|
FINISHED |
| Object | Bayou Bartholomew |
—
|
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: Bayou Bartholomew | Statement: [Hamburg, Arkansas, hasAreaWaterBodyNearby, Bayou Bartholomew]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAreaWaterBodyNearby Context triple: [Hamburg, Arkansas, hasAreaWaterBodyNearby, Bayou Bartholomew]
-
A.
hasNearbyWater
chosen
Indicates that one entity is located close to a body of water associated with or relevant to another entity.
-
B.
hasAreaWaterBody
Indicates that an entity includes, contains, or is associated with a body of water within its area or boundaries.
-
C.
hasAssociatedWaterBody
Indicates that one entity is linked to, or occurs in connection with, a specific body of water such as a river, lake, or sea.
-
D.
hasNearbyWaterBoard
Indicates that an entity is located close to, or within the jurisdictional area of, a water management or water regulatory board.
-
E.
hasNearbyWaterInfrastructure
Indicates that a location or entity is situated close to water-related infrastructure such as pipes, treatment facilities, or distribution systems.
- 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_69ef59092c8881908114ad184248cc46 |
completed | April 27, 2026, 12:39 p.m. |
| NER | Named-entity recognition | batch_69f6e6029a10819098ff21f58079e70e |
completed | May 3, 2026, 6:06 a.m. |
| PD | Predicate disambiguation | batch_69f6e3d5e8188190b1e1c2e5d1b77031 |
completed | May 3, 2026, 5:57 a.m. |
Created at: April 27, 2026, 2:22 p.m.