Triple
T3267545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chicago community areas |
E68562
|
entity |
| Predicate | haveCharacteristic |
P274
|
FINISHED |
| Object | geographically distinct divisions of the 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: geographically distinct divisions of the city | Statement: [Chicago community areas, haveCharacteristic, geographically distinct divisions of the city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: haveCharacteristic Context triple: [Chicago community areas, haveCharacteristic, geographically distinct divisions of the city]
-
A.
hasCharacteristic
chosen
Indicates that an entity possesses, exhibits, or is defined by a particular attribute, feature, or quality.
-
B.
hasBluetooth
Indicates that one entity is equipped with or supports Bluetooth connectivity.
-
C.
hasMusicCharacteristic
Indicates that an entity possesses a specific musical feature, quality, or attribute.
-
D.
hasSensor
Indicates that one entity is equipped with, contains, or uses a particular sensor.
-
E.
hasFeatureCode
Indicates that an entity is associated with a specific feature identifier or code that characterizes one of its properties or attributes.
- 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_69ad8590444081909e8107a8aeef3a23 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adafcf9c6c819092f9c618b778b46d |
completed | March 8, 2026, 5:20 p.m. |
| PD | Predicate disambiguation | batch_69ada41d7eac8190ada4bf5f793d5c49 |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:09 p.m.