Triple
T26963433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarah Jane Negley Mellon |
E679107
|
entity |
| Predicate | birthNameContains |
P9161
|
FINISHED |
| Object | Negley |
—
|
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: Negley | Statement: [Sarah Jane Negley Mellon, birthNameContains, Negley]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: birthNameContains Context triple: [Sarah Jane Negley Mellon, birthNameContains, Negley]
-
A.
fullNameIncludes
chosen
Indicates that one entity’s full name contains another specified string or name component as a substring.
-
B.
hasNotablePersonWithGivenName
Indicates that there exists a notable person associated with the subject whose given (first) name matches the specified value.
-
C.
hasNotablePersonWithSurname
Indicates that an entity is associated with at least one notable person who bears a specified surname.
-
D.
givenNameIn
Indicates that an entity has a specific first or given name in a particular language or naming context.
-
E.
hasOfficialNameContaining
Indicates that an entity’s official name includes a specified substring or sequence of characters.
- 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_69eeeb4f3a448190b1e94b2d4776c16e |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f620ede4f88190a98f91af97505663 |
completed | May 2, 2026, 4:06 p.m. |
| PD | Predicate disambiguation | batch_69f61b3ee7b08190a0a1bc5d26b757aa |
completed | May 2, 2026, 3:41 p.m. |
Created at: April 27, 2026, 6:33 a.m.