Triple
T24368763
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Dallas |
E614272
|
entity |
| Predicate | distanceToDenton |
P155948
|
FINISHED |
| Object | approximately 15 miles |
—
|
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: approximately 15 miles | Statement: [Lake Dallas, distanceToDenton, approximately 15 miles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToDenton Context triple: [Lake Dallas, distanceToDenton, approximately 15 miles]
-
A.
distanceFromDallas
Indicates the measured distance between a given place or entity and the city of Dallas.
-
B.
distanceToFortWorth
Indicates the spatial distance between a given location and the city of Fort Worth.
-
C.
distanceToAustin
Indicates the spatial distance between a given entity or location and the city of Austin.
-
D.
distanceToFortWorth_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and Fort Worth.
-
E.
distanceToMcKinney
Indicates the measured or calculated distance between a given entity and the location named McKinney.
- 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_69e2d7e1e010819098b95eb3f905943d |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f2938994c081909730d1e02e823dfd |
completed | April 29, 2026, 11:26 p.m. |
| PD | Predicate disambiguation | batch_69f287bb1b2c81909c2e7fcc392ad143 |
completed | April 29, 2026, 10:35 p.m. |
| PDg | Predicate description generation | batch_69f28f4d978c81908310c01def2514cc |
completed | April 29, 2026, 11:07 p.m. |
Created at: April 18, 2026, 2:01 a.m.