Triple
T23415509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Muggsy Spanier |
E560194
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Spanier |
—
|
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: Spanier | Statement: [Muggsy Spanier, familyName, Spanier]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Spanier Context triple: [Muggsy Spanier, familyName, Spanier]
-
A.
Spanier
chosen
Spanier is a surname most prominently associated with Graham Spanier, the former president of Pennsylvania State University.
-
B.
Ispagnac
Ispagnac is a commune in southern France known for its picturesque setting in the Gorges du Tarn within the Lozère department.
-
C.
Gallego
Gallego is a Spanish surname commonly associated with people of Galician origin or ancestry.
-
D.
Bourbon Spain
Bourbon Spain was the early 18th-century Spanish monarchy under the French-origin Bourbon dynasty, marked by centralizing reforms and involvement in the War of the Spanish Succession.
-
E.
Spangenberg
Spangenberg is a small town in Germany, historically situated within the region of Westphalia.
- 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_69e2454b3a5881909c64773dc8a5d289 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1a515fe048190adefdefaeff76cfd |
completed | April 29, 2026, 6:28 a.m. |
Created at: April 17, 2026, 5:39 p.m.