Triple
T17549725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leland H. Hartwell |
E427426
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Leland |
—
|
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: Leland | Statement: [Leland H. Hartwell, givenName, Leland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leland Context triple: [Leland H. Hartwell, givenName, Leland]
-
A.
Leland
Leland is a residential neighborhood located within the city of Compton, California.
-
B.
Leland
chosen
Leland is a masculine given name of English origin, historically associated with figures such as American industrialist and Stanford University founder Leland Stanford.
-
C.
Eldridge
Eldridge is an English-language surname of Old English origin, borne by various notable individuals across fields such as politics, the arts, and sports.
-
D.
Seymour
Seymour is a masculine given name of English origin that has been borne by various notable figures in fields such as business, politics, and the arts.
-
E.
Seymour
Seymour is a socially awkward, middle-aged record collector who becomes a central figure in the coming-of-age graphic novel and film "Ghost World."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d889df6dc081908f67dbadc03c07ee |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e45463ddf88190a2c29f3246adcb6e |
completed | April 19, 2026, 4:04 a.m. |
Created at: April 10, 2026, 5:50 a.m.