Triple
T11176950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bay of Montevideo |
E264435
|
entity |
| Predicate | hasMouthWidth |
P98275
|
FINISHED |
| Object | approximately 2.5 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 2.5 km | Statement: [Bay of Montevideo, hasMouthWidth, approximately 2.5 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMouthWidth Context triple: [Bay of Montevideo, hasMouthWidth, approximately 2.5 km]
-
A.
hasTypeOfMouth
Indicates that an entity possesses a mouth characterized by a specific type or form.
-
B.
hasMultipleMouths
Indicates that an entity possesses more than one mouth.
-
C.
hasPortNearMouth
Indicates that an entity possesses a port or opening located close to its mouth region.
-
D.
hasMouthElevationApprox
Indicates an approximate vertical height or elevation value associated with the mouth of something (such as a feature, opening, or structure).
-
E.
hasScenicMouth
Indicates that an entity’s mouth is visually attractive or aesthetically pleasing to look at.
- 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_69d6aa9dafac8190bd90d2c74f661aa7 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8987e1081909b28a0bdb866beae |
completed | April 9, 2026, 5:57 p.m. |
| PD | Predicate disambiguation | batch_69d75cf0e6e88190973694abe2990973 |
completed | April 9, 2026, 8:01 a.m. |
| PDg | Predicate description generation | batch_69d7706116248190a87440bec3960884 |
completed | April 9, 2026, 9:24 a.m. |
Created at: April 8, 2026, 9:29 p.m.