Triple
T2906874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Francisco |
E62786
|
entity |
| Predicate | hasShortForm |
P43
|
FINISHED |
| Object | Fran |
E77813
|
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: Fran | Statement: [Francisco, hasShortForm, Fran]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fran Context triple: [Francisco, hasShortForm, Fran]
-
A.
Fran
chosen
Fran is a common shortened given name, typically used as a diminutive of Frances or Francis.
-
B.
Foix
Foix is a historic town in southwestern France known for its medieval castle and role as the former capital of the County of Foix.
-
C.
Franca
Franca is a city in the northeastern part of the Brazilian state of São Paulo, known historically for its leather and footwear industry.
-
D.
The French
The French is a renowned fine-dining restaurant in Manchester’s Midland Hotel, known for its modern British cuisine and historic, elegant setting.
-
E.
France
France is a major Western European nation known for its influential history, culture, and economy, and as a founding member of the European Union and the United Nations.
- 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_69ab4c3e070c8190b78d3d2c005876dd |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abe0d0628c81909680af2f0db2ecae |
completed | March 7, 2026, 8:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b05612e79081908c962c2fe2e362d6 |
completed | March 10, 2026, 5:34 p.m. |
Created at: March 6, 2026, 10:11 p.m.