Triple
T15456456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord Edmund Howard |
E371781
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Howard |
E118997
|
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: [Lord Edmund Howard, familyName, Howard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Howard Context triple: [Lord Edmund Howard, familyName, Howard]
-
A.
Howard
Howard is the given name of the influential American film director, producer, and screenwriter Howard Hawks.
-
B.
Howard
chosen
Howard is a common English surname shared by numerous notable figures across entertainment, politics, and other fields.
-
C.
Howard
Howard is a small rural town in Queensland, Australia, known historically for coal mining and situated within the Fraser Coast Region.
-
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.
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_69d85cc8bd308190886949510b42e764 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f146a2c8190882741af3ec15268 |
completed | April 16, 2026, 1:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff21b7b600819081a6087bd6309237 |
completed | May 9, 2026, 11:59 a.m. |
Created at: April 10, 2026, 3:31 a.m.