Triple
T60648
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Isthmus of Panama |
E1204
|
entity |
| Predicate | hasMinimumWidthApproximately |
P619
|
FINISHED |
| Object | 50 kilometers |
—
|
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: 50 kilometers | Statement: [Isthmus of Panama, hasMinimumWidthApproximately, 50 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMinimumWidthApproximately Context triple: [Isthmus of Panama, hasMinimumWidthApproximately, 50 kilometers]
-
A.
typicalHeight
Indicates the usual or characteristic height associated with an entity, such as a person, object, or species.
-
B.
hasMaximumDepth
Indicates that an entity possesses a greatest or limiting depth value beyond which it does not extend.
-
C.
width
chosen
Indicates the measurement of how wide an entity is, typically the extent of its horizontal dimension from side to side.
-
D.
hasNumberOfScreens
Indicates the quantity of screens associated with or contained in a given entity.
-
E.
hasNumberOfCasesApprox
Indicates that an entity is associated with an approximate (not exact) count of cases.
- 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_69a24ba4f760819081f6638a3c70538a |
completed | Feb. 28, 2026, 1:57 a.m. |
| NER | Named-entity recognition | batch_69a251a1b8ac8190b44be4c3c41e5681 |
completed | Feb. 28, 2026, 2:23 a.m. |
| PD | Predicate disambiguation | batch_69a24ea0bec48190b2af1fb287e9e692 |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:02 a.m.