Triple
T3757507
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Family Plot |
E82082
|
entity |
| Predicate | featuresCharacter |
P626
|
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: [Family Plot, featuresCharacter, Fran]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fran Context triple: [Family Plot, featuresCharacter, Fran]
-
A.
Fran
chosen
Fran is a common shortened given name, typically used as a diminutive of Frances or Francis.
-
B.
Fransat
Fransat is a French free-to-air satellite television platform that provides access to the national digital terrestrial TV channels across France.
-
C.
France Gall
France Gall was a popular French yé-yé singer and Eurovision winner who became a major figure in French pop music from the 1960s onward.
-
D.
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.
-
E.
Franca
Franca is a city in the northeastern part of the Brazilian state of São Paulo, known historically for its leather and footwear 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_69ad8b1db40081908b61ffa6b78afd4d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcbc04d348190b0e4a90d18bdd160 |
completed | March 8, 2026, 7:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4e50f77fc8190b7774a7359118c9c |
completed | March 14, 2026, 4:33 a.m. |
Created at: March 8, 2026, 3:35 p.m.