Triple
T4097397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juigalpa |
E87854
|
entity |
| Predicate | distanceFromManagua |
P53357
|
FINISHED |
| Object | approximately 140 kilometers |
—
|
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 140 kilometers | Statement: [Juigalpa, distanceFromManagua, approximately 140 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromManagua Context triple: [Juigalpa, distanceFromManagua, approximately 140 kilometers]
-
A.
distanceToGuatemalaCity
Indicates the spatial distance between a given location and Guatemala City.
-
B.
distanceFromMexicoCity
Indicates the measured distance between an entity’s location and Mexico City.
-
C.
distanceToPort-au-Prince
Indicates the spatial distance between a given location and the city of Port-au-Prince.
-
D.
distanceToHavana
Indicates the measured spatial distance between a given entity’s location and the city of Havana.
-
E.
distanceFromCharlotteAmalie
Indicates the measured distance between a given location and Charlotte Amalie.
- 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_69aed94564cc8190a9c1457daedb6e7f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefce017708190a4d33753fd32a7bb |
completed | March 9, 2026, 5:01 p.m. |
| PD | Predicate disambiguation | batch_69aef90b2ef08190ae84febfd69dd48b |
completed | March 9, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69aefa5c52648190b001027f4dba75cb |
completed | March 9, 2026, 4:50 p.m. |
Created at: March 9, 2026, 3:40 p.m.