Triple
T2873421
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ted Lerner |
E56818
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Theodore |
E55214
|
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: Theodore | Statement: [Ted Lerner, givenName, Theodore]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Theodore Context triple: [Ted Lerner, givenName, Theodore]
-
A.
Theodore
chosen
Theodore is a masculine given name of Greek origin, meaning "gift of God," from which the nickname Ted is derived.
-
B.
Theodor
Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
-
C.
Theodor
Theodor is the given name of Emil Theodor Kocher, a Swiss surgeon and Nobel laureate renowned for his pioneering work in thyroid surgery.
-
D.
Theodore Reeves
Theodore Reeves was a screenwriter best known for his work on classic Hollywood films, including the beloved horse-racing drama "National Velvet."
-
E.
Benjamin
Benjamin is the given name of Benjamin "Bugsy" Siegel, the notorious American mobster who played a key role in the development of Las Vegas.
- 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_69ab4a4ced288190ab6d3e062d10f7f6 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abdfe59ef88190b8bdfdd03e8965f3 |
completed | March 7, 2026, 8:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b03153b5048190925bfacc07f2db66 |
completed | March 10, 2026, 2:57 p.m. |
Created at: March 6, 2026, 10:03 p.m.