Triple
T20638755
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jade Angelou |
E507159
|
entity |
| Predicate | setInWork |
P2011
|
FINISHED |
| Object | San Francisco |
—
|
NE NERFINISHED |
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: San Francisco | Statement: [Jade Angelou, setInWork, San Francisco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Francisco Context triple: [Jade Angelou, setInWork, San Francisco]
-
A.
San Francisco
San Francisco is a major coastal city in Northern California known for its hilly landscape, iconic Golden Gate Bridge, and role as a historic center of technology and counterculture.
-
B.
San Francisco
San Francisco is a coastal municipality in the province of Southern Leyte in the Philippines, known for its rural communities and proximity to the Bohol Sea.
-
C.
San Francisco
San Francisco is a coastal municipality in the Philippine province of Surigao del Norte, known for its island landscapes and fishing communities.
-
D.
San Francisco
San Francisco is a town located in the Atlántida Department on the northern Caribbean coast of Honduras.
-
E.
San Francisco
San Francisco is a municipality in the Philippine province of Quezon known for its rural communities and agricultural economy.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
Provenance (2 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_69e0b4be702c8190a3d2410a881d310a |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6ad1163008190aa9df36750a952d2 |
completed | April 20, 2026, 10:47 p.m. |
Created at: April 16, 2026, 11:42 a.m.