Triple
T15557083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Foster McCreight |
E370896
|
entity |
| Predicate | middleName |
P143
|
FINISHED |
| Object | Foster |
E25127
|
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: Foster | Statement: [John Foster McCreight, middleName, Foster]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Foster Context triple: [John Foster McCreight, middleName, Foster]
-
A.
Foster
chosen
Foster is a common English surname borne by numerous notable individuals across fields such as entertainment, sports, politics, and academia.
-
B.
Foster
Foster is a small rural town in western Rhode Island known for its forests, historic character, and low population density.
-
C.
Fowler
Fowler is a surname of English origin borne by numerous notable individuals across fields such as engineering, politics, sports, and the arts.
-
D.
Torey
Torey is a given name, typically used as a variant of names like Tore or Tory.
-
E.
Fay
Fay is a given name most famously associated with Canadian-American actress Fay Wray, the iconic star of the 1933 film "King Kong."
- 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_69d85cc6cf40819091f4a5facee1ebe6 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04a97dbfc8190a98cbbac5e71ba88 |
completed | April 16, 2026, 2:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff4c4046c08190a43bd5577a97f33d |
completed | May 9, 2026, 3:01 p.m. |
Created at: April 10, 2026, 4:09 a.m.