Triple
T170808
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Edwin Booth |
E3117
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Edwin |
E481
|
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: Edwin | Statement: [Edwin Booth, givenName, Edwin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Edwin Context triple: [Edwin Booth, givenName, Edwin]
-
A.
Edwin
chosen
Edwin is a masculine given name of Old English origin meaning "rich friend" or "prosperous friend."
-
B.
Adelbert
Adelbert is a masculine given name of German origin, historically borne by figures such as the poet and naturalist Adelbert von Chamisso.
-
C.
Kenneth
Kenneth is the formal given name of American country music singer, songwriter, and actor Kenny Rogers.
-
D.
Herbert
Herbert is a masculine given name of Germanic origin that has been borne by various notable figures, including U.S. President Herbert Hoover.
-
E.
Erwin
Erwin is a masculine given name of German origin, historically associated with figures such as the World War II field marshal Erwin Rommel.
- 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_69a2524ce1e48190ab066bf72859f474 |
completed | Feb. 28, 2026, 2:26 a.m. |
| NER | Named-entity recognition | batch_69a258b82bdc81908ebd50fb05d511df |
completed | Feb. 28, 2026, 2:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3a881821c81908272faf90952fc97 |
completed | March 1, 2026, 2:46 a.m. |
Created at: Feb. 28, 2026, 2:34 a.m.