Triple
T24499017
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Corofin |
E617878
|
entity |
| Predicate | distanceToEnnis_km |
P143122
|
FINISHED |
| Object | approximately 13 |
—
|
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 13 | Statement: [Corofin, distanceToEnnis_km, approximately 13]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToEnnis_km Context triple: [Corofin, distanceToEnnis_km, approximately 13]
-
A.
distanceToEnnis
chosen
Indicates the spatial distance between a given entity or location and the place named Ennis.
-
B.
distanceToFortWorth_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and Fort Worth.
-
C.
distanceToEdinburgh_km
Indicates the physical distance, measured in kilometers, between an entity’s location and Edinburgh.
-
D.
distanceFromGillam_km
Indicates the distance, measured in kilometers, between an entity and the location Gillam.
-
E.
distanceTo_Ladysmith_km
Indicates the measured distance, in kilometers, between a given entity’s location and Ladysmith.
- 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_69e2d7f682108190a1a7ca5fd485ee8a |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f2a9d912e88190bc39c05a9d7f407e |
completed | April 30, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69f2a6a4580481908fddc385f5262f95 |
completed | April 30, 2026, 12:47 a.m. |
Created at: April 18, 2026, 2:23 a.m.