Triple
T4901054
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ted Belytschko |
E109798
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Ted |
E7779
|
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: Ted | Statement: [Ted Belytschko, givenName, Ted]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ted Context triple: [Ted Belytschko, givenName, Ted]
-
A.
Ted
chosen
Ted is a masculine given name, often a diminutive of Theodore or Edward, commonly used in English-speaking countries.
-
B.
Ted
Ted is a 2012 comedy film about a foul-mouthed living teddy bear, created by and starring Seth MacFarlane.
-
C.
Tom
Tom is a common masculine given name, often used in English-speaking countries as a short form of Thomas.
-
D.
Tony
The Tony is a prestigious American theater award presented annually to recognize excellence in Broadway productions.
-
E.
Tony
Tony is the central romantic lead in the musical "The Most Happy Fella," an aging Italian-American vintner whose love story drives the plot.
- 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_69bd441180708190ba42ffb44fea533a |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6e4dd6bc819094b1cbf533510995 |
completed | March 20, 2026, 3:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be6fd2a0348190a285ea1a62e7ae1b |
completed | March 21, 2026, 10:15 a.m. |
Created at: March 20, 2026, 1:28 p.m.