Triple
T1029885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jhansi |
E22224
|
entity |
| Predicate | distanceToLucknow |
P24190
|
FINISHED |
| Object | approximately 320 km |
—
|
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 320 km | Statement: [Jhansi, distanceToLucknow, approximately 320 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToLucknow Context triple: [Jhansi, distanceToLucknow, approximately 320 km]
-
A.
distanceToDelhiByRail_km
Indicates the distance, measured in kilometers, from a given place to Delhi when traveling by rail.
-
B.
distanceToDelhiByRoad_km
Indicates the road travel distance, measured in kilometers, from a given place to Delhi.
-
C.
distanceFromKolkata
Indicates the spatial distance between a given location and the city of Kolkata.
-
D.
distanceToLeipzig
Indicates the spatial distance between a given entity’s location and the city of Leipzig.
-
E.
distanceToBerlin
Indicates the spatial distance between a given entity’s location and the city of Berlin.
- 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_69a493d848848190aed4011b34b2e8d3 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b95d35888190a20593a278175df7 |
completed | March 1, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69a4b7276180819085c6b23501a6a6e0 |
completed | March 1, 2026, 10:01 p.m. |
| PDg | Predicate description generation | batch_69a4b95bb5908190ad1d5f5e0d8f664d |
completed | March 1, 2026, 10:10 p.m. |
Created at: March 1, 2026, 7:41 p.m.