Triple
T11430808
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Howard |
E270873
|
entity |
| Predicate | title |
P38
|
FINISHED |
| Object | Howard |
unclear NED1
|
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: Howard | Statement: [Howard, title, Howard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Howard Context triple: [Howard, title, Howard]
-
A.
Howard
Howard is the middle name of William Howard Taft, the 27th president of the United States and later Chief Justice of the Supreme Court.
-
B.
Howard
Howard is the given first name of Ward Cunningham, the American computer programmer best known for creating the first wiki.
-
C.
Howard
Howard is a seasoned, wise old prospector in the classic 1948 film "The Treasure of the Sierra Madre," known for his experience, moral insight, and pivotal role in the gold-hunting expedition.
-
D.
Howard
Howard is the middle name of Robert H. Grubbs, the Nobel Prize–winning American chemist renowned for his work on olefin metathesis.
-
E.
Howard
Howard is a character in Kenneth Lonergan's play "The Waverly Gallery," serving as a key figure in the story's exploration of family, memory, and aging.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69d6aadeef688190874bcecd88b3dd9b |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d806c1bfb881909720c74fe0fa837f |
completed | April 9, 2026, 8:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5b8d923688190bb4d61d57768e10e |
completed | April 20, 2026, 5:25 a.m. |
Created at: April 8, 2026, 9:35 p.m.