Triple
T791390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Agra |
E16921
|
entity |
| Predicate | distanceToDelhiByRoad_km |
P19330
|
FINISHED |
| Object | approximately 210 |
—
|
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 210 | Statement: [Agra, distanceToDelhiByRoad_km, approximately 210]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToDelhiByRoad_km Context triple: [Agra, distanceToDelhiByRoad_km, approximately 210]
-
A.
flightDistance
Indicates the measured distance covered by a flight between its origin and destination.
-
B.
distanceTraveled
Indicates the total length of the path an entity has moved over a period of time or between two points.
-
C.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
D.
distanceToBerlin
Indicates the spatial distance between a given entity’s location and the city of Berlin.
-
E.
distanceFromWaggaWagga_km
Indicates the numerical distance, measured in kilometers, between an entity’s location and Wagga Wagga.
- 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_69a4936cb7448190914f5fe4b8d81607 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a798c7608190b9c79c52a1fe0859 |
completed | March 1, 2026, 8:54 p.m. |
| PD | Predicate disambiguation | batch_69a4a50ef72c819084ffe9f31dbd0262 |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a62b497081909503c8d30c7ce1db |
completed | March 1, 2026, 8:48 p.m. |
Created at: March 1, 2026, 7:38 p.m.