Triple
T5892362
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Milhous |
E131019
|
entity |
| Predicate | hasSpelling |
P457
|
FINISHED |
| Object | M-i-l-h-o-u-s |
E23107
|
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: M-i-l-h-o-u-s | Statement: [Milhous, hasSpelling, M-i-l-h-o-u-s]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: M-i-l-h-o-u-s Context triple: [Milhous, hasSpelling, M-i-l-h-o-u-s]
-
A.
Mulhousien
Mulhousien is the French term for an inhabitant or native of the city of Mulhouse in northeastern France.
-
B.
Moulinois
Moulinois is the French term for an inhabitant or native of the town of Moulins in central France.
-
C.
Choulex
Choulex is a small municipality in the canton of Geneva in southwestern Switzerland, known for its rural character and proximity to the city of Geneva.
-
D.
Milhous
chosen
Milhous is the distinctive middle name of Richard Nixon, the 37th president of the United States.
-
E.
Ville Haute
Ville Haute is the historic city-center district of Luxembourg City, known for its medieval fortifications, government buildings, and main shopping streets.
- 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_69c00857439c819095950754176aa58a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c036b5c68481909fdcba428238c74d |
completed | March 22, 2026, 6:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0b14c2ff081908243988d5815be6d |
completed | March 23, 2026, 3:19 a.m. |
Created at: March 22, 2026, 3:58 p.m.