Triple
T16386352
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kfar Shmaryahu |
E397931
|
entity |
| Predicate | distanceToTelAviv |
P40351
|
FINISHED |
| Object | approximately 10–15 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 10–15 km | Statement: [Kfar Shmaryahu, distanceToTelAviv, approximately 10–15 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToTelAviv Context triple: [Kfar Shmaryahu, distanceToTelAviv, approximately 10–15 km]
-
A.
distanceFromTelAviv_km
chosen
Indicates the physical distance, measured in kilometers, between a given place and Tel Aviv.
-
B.
distanceFromBeersheba
Indicates the spatial distance between a given location or entity and Beersheba.
-
C.
distanceFromJerusalem
Indicates the spatial distance between a given location and Jerusalem.
-
D.
roadDistanceToHaifa
Indicates the distance between an entity and Haifa when traveling via the road network rather than in a straight line.
-
E.
distanceToPalestine
Indicates the spatial distance between a given entity and the geographic region of Palestine.
- 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_69d87f2880b48190ae1a9673a3bbef80 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e3263d260081909db9ac6016d5738a |
completed | April 18, 2026, 6:35 a.m. |
| PD | Predicate disambiguation | batch_69e226f94dd48190b7b8e0e983738a67 |
completed | April 17, 2026, 12:26 p.m. |
Created at: April 10, 2026, 5:08 a.m.