Triple
T1356466
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diana, Princess of Wales |
E29000
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Spencer |
E71790
|
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: Spencer | Statement: [Diana, Princess of Wales, familyName, Spencer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Spencer Context triple: [Diana, Princess of Wales, familyName, Spencer]
-
A.
Spencer
chosen
Spencer is a masculine given name of English origin, historically associated with roles such as steward or dispenser and borne by various notable figures.
-
B.
Spencer
Spencer is the middle name of American author and aviator Anne Spencer Lindbergh, reflecting her family’s naming tradition.
-
C.
Spencer Brook
Spencer Brook is a smaller tributary stream that feeds into New York’s Croton River within the Croton River watershed system.
-
D.
Spencer Rivers
Spencer Rivers is the son of American sportscaster and former NBA player Doc Rivers.
-
E.
Parker
Parker is a common English surname borne by numerous notable individuals across fields such as politics, sports, arts, and science.
- 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_69a498d77abc8190913bf57e5f51d2c4 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c28c8dd0819082f94c9e7c837c5f |
completed | March 1, 2026, 10:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acde181b388190a2093f21717d5aa8 |
completed | March 8, 2026, 2:25 a.m. |
Created at: March 1, 2026, 7:56 p.m.