Triple
T3644872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margaret Crocker |
E77273
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Crocker |
E211680
|
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: Crocker | Statement: [Margaret Crocker, familyName, Crocker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Crocker Context triple: [Margaret Crocker, familyName, Crocker]
-
A.
Crocker
chosen
Crocker is a surname of English origin borne by various notable individuals across fields such as technology, sports, and the arts.
-
B.
Sutter
Sutter is a surname of German origin most notably associated with John Sutter, a 19th-century pioneer linked to the California Gold Rush.
-
C.
Cutler
Cutler is the surname of Jay Cutler, a former NFL quarterback best known for his tenure with the Chicago Bears.
-
D.
Riggs
Riggs is a surname most notably associated with American playwright Lynn Riggs, whose work inspired the musical "Oklahoma!".
-
E.
Donner
Donner is one of Santa Claus's traditional flying reindeer, often depicted as helping pull Santa’s sleigh on Christmas Eve.
- 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_69ad85de1b988190a45f8dbfebc806fc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc35da84c81908950de92ba171fa3 |
completed | March 8, 2026, 6:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b44f3207688190b8d93e2e833b15a0 |
completed | March 13, 2026, 5:53 p.m. |
Created at: March 8, 2026, 3:24 p.m.