Triple
T36720509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wildorado, Texas |
E907043
|
entity |
| Predicate | distanceToAmarilloInMiles |
P95850
|
FINISHED |
| Object | about 20 |
—
|
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: about 20 | Statement: [Wildorado, Texas, distanceToAmarilloInMiles, about 20]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToAmarilloInMiles Context triple: [Wildorado, Texas, distanceToAmarilloInMiles, about 20]
-
A.
distanceToAmarillo
chosen
Indicates the spatial distance between a given entity and the location Amarillo.
-
B.
distanceToFortWorth_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and Fort Worth.
-
C.
approximateDistanceToElPaso
Indicates that one entity has an estimated or rough distance measurement relative to the location of El Paso.
-
D.
distanceToFortWorth
Indicates the spatial distance between a given location and the city of Fort Worth.
-
E.
distanceFromSanAngelo
Indicates the measured distance between a given entity and the location of San Angelo.
- 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_69f76e746e4c8190a0d05cc6d57a643e |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0e039481908a4a2666f76c5363 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:12 p.m.