Triple
T15462228
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don Shula |
E371930
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Shula |
E371930
|
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: Shula | Statement: [Don Shula, familyName, Shula]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shula Context triple: [Don Shula, familyName, Shula]
-
A.
Shula
chosen
Shula is a surname most famously associated with Don Shula, the legendary NFL coach of the Miami Dolphins.
-
B.
Syal
Syal is the surname of Meera Syal, a prominent British-Indian comedian, writer, playwright, and actress known for her work in television, film, and literature.
-
C.
Ta’aisha
The Ta’aisha are a Sudanese Arab tribal group from the Darfur–Kordofan region, historically prominent through their leadership role in the Mahdist state under Abdallahi ibn Muhammad.
-
D.
Marlo
Marlo is a small coastal town in East Gippsland, Victoria, Australia, known for its location near the mouth of the Snowy River and its fishing and outdoor recreation.
-
E.
Marlo
Marlo is a fictional character associated with Tully, likely appearing in a narrative centered on that figure.
- 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_69e03f1927708190a0d2b63e75469a0e |
completed | April 16, 2026, 1:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff2cff942081908a2f5351079666a3 |
completed | May 9, 2026, 12:47 p.m. |
Created at: April 10, 2026, 3:32 a.m.