Triple
T3495806
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosa Parks |
E73846
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Tuskegee, Alabama, United States |
E70707
|
NE 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: Tuskegee, Alabama, United States | Statement: [Rosa Parks, placeOfBirth, Tuskegee, Alabama, United States]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tuskegee, Alabama, United States Context triple: [Rosa Parks, placeOfBirth, Tuskegee, Alabama, United States]
-
A.
Tuskegee, Alabama, United States
chosen
Tuskegee, Alabama, United States, is a historically significant Southern city known for its prominent role in African American history, including the Tuskegee Institute and the Tuskegee Airmen.
-
B.
Black, Alabama
Black, Alabama is a small rural town located in southeastern Alabama near the Florida border.
-
C.
Rosa, Alabama
Rosa, Alabama is a small town located in Blount County in the northern part of the state.
-
D.
Steele, Alabama
Steele, Alabama is a small town in northeastern Alabama known for its rural character and location within St. Clair County.
-
E.
Gadsden, Alabama
Gadsden, Alabama is a small industrial city in northeastern Alabama known historically for its manufacturing plants and labor history.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ad85cdb6e48190a335d412b9194ed8 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbbb06fd08190b6c1fadfce4148f0 |
completed | March 8, 2026, 6:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b37e67f68081909766d78ee24e9e6e |
completed | March 13, 2026, 3:03 a.m. |
Created at: March 8, 2026, 3:18 p.m.