Triple
T4725149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Meron |
E104864
|
entity |
| Predicate | rankingByHeightInIsrael |
P59052
|
FINISHED |
| Object | one of the highest peaks in Israel |
—
|
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: one of the highest peaks in Israel | Statement: [Mount Meron, rankingByHeightInIsrael, one of the highest peaks in Israel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingByHeightInIsrael Context triple: [Mount Meron, rankingByHeightInIsrael, one of the highest peaks in Israel]
-
A.
rankByHeightWorld
Indicates an ordering of entities based on their relative height compared to all others in the world.
-
B.
rankAmongTallestBuildings
Indicates that one building is among the tallest buildings within a specified group, area, or category.
-
C.
rankInCityByHeight
Indicates the relative ordering of entities within a specific city based on their height, such as which is tallest, second tallest, and so on.
-
D.
averageHeight
Indicates that the relationship specifies the mean height value calculated from a set of entities or measurements.
-
E.
rankByHeightPakistan
Indicates an ordering of entities based on their height specifically within the context of Pakistan.
- 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_69bd43ed84648190ae0b7ee8e8d00482 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd67c9c3c08190a6c4944cdd1362a8 |
completed | March 20, 2026, 3:29 p.m. |
| PD | Predicate disambiguation | batch_69bd6220071881909670c89d072ffb6d |
completed | March 20, 2026, 3:05 p.m. |
| PDg | Predicate description generation | batch_69bd67c895dc8190ba648002ff54424b |
completed | March 20, 2026, 3:29 p.m. |
Created at: March 20, 2026, 1:18 p.m.