Triple
T10418032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jim Hanifan |
E245570
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Hanifan |
E245570
|
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: Hanifan | Statement: [Jim Hanifan, familyName, Hanifan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hanifan Context triple: [Jim Hanifan, familyName, Hanifan]
-
A.
Hanifan
chosen
Hanifan is a surname most notably associated with Jim Hanifan, an American football coach and former player.
-
B.
Hani
The Hani are an ethnic minority group in China, primarily known for their terraced rice farming, distinctive traditional dress, and rich folk culture in the mountainous regions of Yunnan.
-
C.
Haabneeme
Haabneeme is a small coastal settlement in northern Estonia, located on the Viimsi Peninsula near Tallinn.
-
D.
Afif
Afif is a town in central Saudi Arabia known as an inland community within the Riyadh administrative region.
-
E.
Haan
Haan is a town in the German state of North Rhine-Westphalia, known for its location between Düsseldorf and Wuppertal and its mix of residential areas and light industry.
- 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_69d381be340c8190b05998703d42d224 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4ea2938188190a908316d0a0959be |
completed | April 7, 2026, 11:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d7fc0dd480819082edcc49a245ad4f |
completed | April 9, 2026, 7:20 p.m. |
Created at: April 6, 2026, 12:11 p.m.