Triple
T15485285
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grand Saline, Texas |
E377029
|
entity |
| Predicate | distanceToCantonInMiles |
P118421
|
FINISHED |
| Object | about 15 |
—
|
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 15 | Statement: [Grand Saline, Texas, distanceToCantonInMiles, about 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToCantonInMiles Context triple: [Grand Saline, Texas, distanceToCantonInMiles, about 15]
-
A.
distanceToHartford
Indicates the spatial distance between a given entity’s location and the city of Hartford.
-
B.
distanceFromMajorCity
Indicates the measured distance between a given location and a specified major city.
-
C.
distanceToMilwaukee
Indicates the measured or calculated spatial distance between a given entity’s location and the city of Milwaukee.
-
D.
distanceFromWarren_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the location of Warren.
-
E.
distanceToConcord
Indicates the spatial distance between an entity and the location named Concord.
- 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_69d85cd21dcc81908646251b1c26ea00 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f8f71a08190a440ff19dcc65312 |
completed | April 16, 2026, 1:46 a.m. |
| PD | Predicate disambiguation | batch_69ded2874b788190999158e0f043be21 |
completed | April 14, 2026, 11:49 p.m. |
| PDg | Predicate description generation | batch_69ded5deee00819099fa3e43313312e1 |
completed | April 15, 2026, 12:03 a.m. |
Created at: April 10, 2026, 3:46 a.m.