Triple
T4109126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Howie Rose |
E88525
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Howard |
E277141
|
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: [Howie Rose, givenName, Howard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Howard Context triple: [Howie Rose, givenName, Howard]
-
A.
Howard
Howard is the given name of the influential American film director, producer, and screenwriter Howard Hawks.
-
B.
Howard
Howard is a common English surname shared by numerous notable figures across entertainment, politics, and other fields.
-
C.
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.
-
D.
Howard
chosen
Howard is a masculine given name of Old English origin meaning "high guardian" or "noble watchman," borne by numerous notable figures across politics, arts, and academia.
-
E.
Howard
Howard is one of Sethe’s sons in Toni Morrison’s novel "Beloved," a child whose life is shaped by the trauma and legacy of slavery.
- 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_69aed9484fb881909146f4c772ad277c |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af01da2b88819088a45401c0ec743a |
completed | March 9, 2026, 5:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b56b850d588190a0fc4b784bc7ca4f |
completed | March 14, 2026, 2:07 p.m. |
Created at: March 9, 2026, 3:40 p.m.