Triple
T23124738
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Morgan Saylor |
E576996
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Morgan |
—
|
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: Morgan | Statement: [Morgan Saylor, givenName, Morgan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Morgan Context triple: [Morgan Saylor, givenName, Morgan]
-
A.
Morgan
Morgan is a Chicago 'L' rapid transit station on the Near West Side serving the city's Pink and Green Lines.
-
B.
Morgan
Morgan is a common Welsh surname with deep cultural and historical roots in Wales and among people of Welsh descent.
-
C.
Morgan
chosen
Morgan is a given name most famously associated with acclaimed American actor and narrator Morgan Freeman.
-
D.
Morgan
Morgan is a common English surname borne by numerous notable individuals across fields such as science, finance, and entertainment.
-
E.
Morgan
Morgan is the first-generation microarchitecture used in AMD's Duron processors, designed as a budget-friendly derivative of the Athlon line with reduced cache and simplified features.
- 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_69e245f6c2e881909a228fdcfeb7c7d3 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e53ac288190b27fe8064fb576c2 |
completed | April 29, 2026, 4:51 a.m. |
Created at: April 17, 2026, 3:59 p.m.