Triple
T3495800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosa Parks |
E73846
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Rosa |
E223709
|
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: Rosa | Statement: [Rosa Parks, givenName, Rosa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rosa Context triple: [Rosa Parks, givenName, Rosa]
-
A.
Rosa
Rosa is a genus of flowering plants known for its ornamental roses, prized worldwide for their beauty, fragrance, and cultural symbolism.
-
B.
Rosa
chosen
Rosa is the birth name of Linda Christian, a Mexican film actress known as the first "Bond girl" for her role in the 1954 television adaptation of Casino Royale.
-
C.
Rosa
Rosa is a celebrated poem by Nikki Giovanni that honors civil rights icon Rosa Parks and reflects on the broader struggle for racial justice.
-
D.
Rosera
Rosera is a town in the Samastipur district of Bihar, India, known as a local commercial and administrative center.
-
E.
Rosa Gryphus
Rosa Gryphus is a central character in Alexandre Dumas' novel "The Black Tulip," known for her devotion, courage, and pivotal role in aiding the protagonist amid political intrigue.
- 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_69b373cce4008190beb010b171bc4940 |
completed | March 13, 2026, 2:17 a.m. |
Created at: March 8, 2026, 3:18 p.m.