Triple
T7375824
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chapala |
E170119
|
entity |
| Predicate | distanceToGuadalajara_km |
P76650
|
FINISHED |
| Object | approximately 50 |
—
|
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 50 | Statement: [Chapala, distanceToGuadalajara_km, approximately 50]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToGuadalajara_km Context triple: [Chapala, distanceToGuadalajara_km, approximately 50]
-
A.
distanceFromOaxacaCity
Indicates the spatial distance between a given place and Oaxaca City.
-
B.
distanceFromMexicoCity
Indicates the measured distance between an entity’s location and Mexico City.
-
C.
distanceFromTijuana
Indicates the measured spatial distance between a given place or entity and the city of Tijuana.
-
D.
distanceFromTucson
Indicates the spatial distance between a given entity and the location of Tucson.
-
E.
distanceFromLaPazApproximate
Indicates an approximate measure of how far something is from La Paz, typically expressed as a rough distance rather than an exact value.
- 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_69c68a5bfaac81909ce7f001dfb70c76 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f1a780f88190abf11994e307b6ad |
completed | March 27, 2026, 9:07 p.m. |
| PD | Predicate disambiguation | batch_69c6f02ee3e08190a7a00c981129b22c |
completed | March 27, 2026, 9:01 p.m. |
| PDg | Predicate description generation | batch_69c6f0ebf9c88190af5a4d87d3fd338a |
completed | March 27, 2026, 9:04 p.m. |
Created at: March 27, 2026, 3:07 p.m.