Triple
T12167080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lingleville, Texas |
E289860
|
entity |
| Predicate | distanceToStephenvilleInMiles |
P103113
|
FINISHED |
| Object | approximately 10 |
—
|
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 10 | Statement: [Lingleville, Texas, distanceToStephenvilleInMiles, approximately 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToStephenvilleInMiles Context triple: [Lingleville, Texas, distanceToStephenvilleInMiles, approximately 10]
-
A.
distanceToFortWorth_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and Fort Worth.
-
B.
distanceToLivingston
Indicates the measured distance between a given entity or location and Livingston.
-
C.
distanceToMcKinney
Indicates the measured or calculated distance between a given entity and the location named McKinney.
-
D.
distanceToAmarillo
Indicates the spatial distance between a given entity and the location Amarillo.
-
E.
distanceToAustin
Indicates the spatial distance between a given entity or location and the city of Austin.
- 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_69d6ab4d6c00819095a9a7c35de83cfb |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91621ca6c81908365732f361aef13 |
completed | April 10, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69d9150e85348190b9b47cda4a17dcd0 |
completed | April 10, 2026, 3:19 p.m. |
| PDg | Predicate description generation | batch_69d916165c708190bf0745e125589f46 |
completed | April 10, 2026, 3:24 p.m. |
Created at: April 8, 2026, 9:50 p.m.