Triple
T18852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wes Wise |
E372
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Wes |
E372
|
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: Wes | Statement: [Wes Wise, givenName, Wes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wes Context triple: [Wes Wise, givenName, Wes]
-
A.
Wes Wise
chosen
Wes Wise is an American journalist and politician who served as mayor of Dallas, Texas, in the 1970s.
-
B.
Andrew
Andrew is a masculine given name of Greek origin meaning "manly" or "brave," widely used in English-speaking countries and beyond.
-
C.
Lee
Lee is a given name shared by numerous individuals across different cultures and professions.
-
D.
Edwin
Edwin is a masculine given name of Old English origin meaning "rich friend" or "prosperous friend."
-
E.
Rogers
Rogers is a common English-language surname borne by numerous notable individuals across fields such as science, politics, entertainment, and sports.
- 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_69a240778d288190815c0052ebbbcc91 |
completed | Feb. 28, 2026, 1:10 a.m. |
| NER | Named-entity recognition | batch_69a2465d9038819087f875a5afac9541 |
completed | Feb. 28, 2026, 1:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a248e73c6c8190a83d10709aa3f9c7 |
completed | Feb. 28, 2026, 1:46 a.m. |
Created at: Feb. 28, 2026, 1:14 a.m.