Triple
T216872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juneau |
E4123
|
entity |
| Predicate | areaCharacteristic |
P3938
|
FINISHED |
| Object | large land area for a U.S. city |
—
|
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: large land area for a U.S. city | Statement: [Juneau, areaCharacteristic, large land area for a U.S. city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaCharacteristic Context triple: [Juneau, areaCharacteristic, large land area for a U.S. city]
-
A.
roofFeature
Indicates that one entity is a feature, element, or characteristic that is part of or associated with a roof.
-
B.
zoningCharacter
Indicates how the regulatory or functional nature of a geographic area is defined or classified in terms of land-use zoning.
-
C.
serviceAreaCharacteristic
chosen
Indicates a relationship where a service area is associated with a specific attribute or feature that characterizes it.
-
D.
amenity
Indicates that one entity provides a useful facility, service, or feature that enhances the convenience or comfort of another entity.
-
E.
hasInteriorFeature
Indicates that an entity contains or includes a specific feature within its interior space.
- 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_69a2573508588190b522c2476d91acfe |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25c4edfa081909fe97c86c3c7801d |
completed | Feb. 28, 2026, 3:09 a.m. |
| PD | Predicate disambiguation | batch_69a25b5357bc8190b29a48e3053fb76d |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.