Triple
T1788166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kültepe |
E39434
|
entity |
| Predicate | distanceFromKayseri |
P32397
|
FINISHED |
| Object | approximately 20 kilometers northeast |
—
|
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 20 kilometers northeast | Statement: [Kültepe, distanceFromKayseri, approximately 20 kilometers northeast]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromKayseri Context triple: [Kültepe, distanceFromKayseri, approximately 20 kilometers northeast]
-
A.
distanceToIstanbulApproxKm
Indicates the approximate distance, measured in kilometers, between a given place and Istanbul.
-
B.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
C.
distanceFromTokyo
Indicates the physical distance between a given location and Tokyo.
-
D.
distanceFromJerusalem
Indicates the spatial distance between a given location and Jerusalem.
-
E.
distanceFromCairo
Indicates the measured spatial distance between a given entity’s location and the city of Cairo.
- 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_69a88631854081909723959921e45c2b |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab75457e54819096b8c6ae8c65550c |
completed | March 7, 2026, 12:45 a.m. |
| PD | Predicate disambiguation | batch_69aa61d165688190924962a98e07ff69 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab75444d28819091c393e62fc97f82 |
completed | March 7, 2026, 12:45 a.m. |
Created at: March 4, 2026, 7:32 p.m.