Triple
T23986351
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bois d'Arc Lake |
E604945
|
entity |
| Predicate | waterSupplyPopulationServed |
P154141
|
FINISHED |
| Object | over 2 million people |
—
|
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: over 2 million people | Statement: [Bois d'Arc Lake, waterSupplyPopulationServed, over 2 million people]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: waterSupplyPopulationServed Context triple: [Bois d'Arc Lake, waterSupplyPopulationServed, over 2 million people]
-
A.
waterSupplyShare
Indicates the proportion of a total water supply that is allocated to or used by a particular entity.
-
B.
sourceOfWaterSupply
Indicates that one entity serves as the origin or provider of another entity’s water supply.
-
C.
waterSourceDedicatedTo
Indicates that a particular water source is specifically allocated or reserved for a designated use, group, or purpose.
-
D.
designedDrinkingWaterPopulation
Indicates that something is intended or planned to serve as drinking water for a specified population.
-
E.
waterUsedByCity
Indicates the amount of water consumed or utilized by a specific city.
- F. None of above. chosen
Provenance (4 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_69e295463f7c8190b1c19dbd114641b9 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d2c1d4508190905a3eb2d98a248b |
completed | April 29, 2026, 9:43 a.m. |
| PD | Predicate disambiguation | batch_69f161578d54819084a8b35496299993 |
completed | April 29, 2026, 1:39 a.m. |
| PDg | Predicate description generation | batch_69f167dca3608190ace9d2eef56b2af6 |
completed | April 29, 2026, 2:07 a.m. |
Created at: April 17, 2026, 9:36 p.m.